Hello everyone This survey is for an in-depth work for my school. I need to do this work to complete my apprenticeship. The work and this survey is about the Ethics of Machine Learning. You should be able to complete my survey in less than ten minutes. Also, the survey does not require any registration or disclosing personally-identifiable information (PII) and no question is mandatory. I’d appreciate it if you could kindly take the time to answer the survey. Survey Link: https://forms.gle/bqh8af8vK9as13UD6 submitted by /u/XDeadScorpion [link] [comments]

Hey guys, My GAN models take 10GB VRAM when I try to generate a 512x512 image. We need to scale stuff up and I plan on productionizing this on a kubernetes cluster but want to optimize them first to save on costs. Ideally I want to run multiple inferences on the same machine without facing a CUDA OUT OF MEMORY ERROR. What are some good ways to optimize VRAM usage? submitted by /u/cbsudux [link] [comments]

I am working on a college project where I decide I would do something like this but I have no vague idea how to do this, I have just started taking AI Ml Andrew Yang classes and I can't figure out how to do this. .can anyone help? Please submitted by /u/youvcan [link] [comments]

submitted by /u/downtownslim [link] [comments]

I've been coming across a few papers in few-shot learning that claim to be under the paradigm of "true few-shot learning." Inspired by the paper True Few-Shot Learning with Language Models (Perez et al., 2021). The basic claim is that the current N-way K-shot setting that's widely used in few-shot tasks is unrealistic due to the fact that the models still have access to many data points in other classes. Not to mention that these models also usually have access to a development set to tune their hyperparameters. The authors claim that these assumptions are unrealistic on the grounds that assuming access to other classes' data points is unrealistic and that if we have access to a dev set the…

I have a set of images with title captions. I'd like to do a joint classification task using both inputs but I haven't seen a commercial autoML solution to train it. Does anyone know of any solution? submitted by /u/somethingstrang [link] [comments]

I’ve noticed a lot of developers at my work use and prefer MacBooks. What do you prefer? submitted by /u/MGeeeeeezy [link] [comments]

In my org, we are highly reliant on Elastic Search and I'm currently investigating the merit of incorporating a semantic search component to our search pipeline. Like most, I've built prototypes leveraging FAISS on our data and while the results are impressive, I haven't come across any compelling use cases where teams are putting this to use at scale. I want to note, I don't come from a research background. submitted by /u/madzthakz [link] [comments]

So as many probably know Google is creating Pathways which should be a model that can do many things, but they haven't released any details or a timeline. I'm wondering if there are other generalizable learners to use as of now. submitted by /u/jmoincali [link] [comments]

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

Understanding the building blocks and design choices of graph neural networks.

What components are needed for building learning algorithms that leverage the structure and properties of graphs?

After five years, Distill will be taking a break.

Reprogramming Neural CA to exhibit novel behaviour, using adversarial attacks.

Weights in the final layer of common visual models appear as horizontal bands. We investigate how and why.

When a neural network layer is divided into multiple branches, neurons self-organize into coherent groupings.

We report the existence of multimodal neurons in artificial neural networks, similar to those found in the human brain.

Neural Cellular Automata learn to generate textures, exhibiting surprising properties.

Neural Cellular Automata learn to generate textures, exhibiting surprising properties.

We present techniques for visualizing, contextualizing, and understanding neural network weights.

Fig 2: A collection of outputs from different post-hoc interpretability techniques.

It becomes possible to evaluate different post-hoc interpretability techniques on the same underlying model, giving us more nuanced insights.

The same will be attested to, by interpretability methods as well as the target output do get influenced by perturbing these attributes.

CitationFor attribution in academic contexts or books, please cite this work asNirmal Sobha Kartha, "Explain Yourself - A Primer on ML Interpretability & Explainability", The Gradient, 2021.

BibTeX citation:@article{kartha2021explain,author = {Kartha, Nirmal Sobha},title = {Explain Yourself - A Primer on ML Interpretability & Explaina…

Why models must be learned and dynamically composedThe models that AI will use to reason, act, and communicate must be learned.

We can divide current models in AI into two classes: neural-network models and representation-based models.

Researchers often divide models into subsymbolic and symbolic models, but this work focuses on model composability, so dividing models into neural-network models and representation-based models is helpful, as we will see.

CitationFor attribution in academic contexts or books, please cite this work asJonathan Mugan, "Strong AI Requires Autonomous Building of Composable Models", The Gradient, 2021.

BibTeX citation:@article{mugan2021strong,author = {Mugan, Jonat…

Given this potential, we see foundation models as the subject of a growing paradigm shift, where many AI systems across domains will directly build upon or heavily integrate foundation models.

Foundation models are not just large language models; foundation models can also be trained using images, video, and other sensory and knowledge base data—and some already are.

People create the data that underpins foundation models, develop foundation models, adapt foundation models for specific applications, and interact with the resulting applications.

Foundation models are a strict superset of LLMs, though the most salient foundation models currently are LLMs (e.g., GPT-3).

Akin to how deep learni…

Sustainable AI can be thought of as another dimension along which we can guide the development of AI systems in addition to typical functional and business requirements.

Share the idea of sustainable AI widelyWe can begin first by sharing this idea of sustainable AI with our own communities of research and practice.

This will create a virtuous cycle of practice and reward enabling a transition into “Green AI”, AI systems that are built and used with the awareness of their carbon impact, as opposed to “Red AI” , AI systems that are built and used with only performance goals in mind ,which is currently favored and supported in the research and practice ecosystem.

Instrument your AI systems to…

They coined a new term, “Foundation Models” to characterize the new paradigm, joined forces in a “Center for Research on Foundation Models”, and published the massive 212-page report “On the Opportunities and Risks of Foundation Models.”Although the term is new, the general approach is not.

At the Workshop on Foundation Models, Jitendra Malik, a renowned expert in computer vision at Berkeley, said, “I am going to take a ... strongly critical role, when we talk about them as the foundation of AI ...

As Georgia Tech professor Mark Riedl wrote on Twitter “Branding very large pre-trained neural language models as “foundation” models is a brilliant … PR stunt.

The report says, unironically, “we …

An Introduction to AI Story GenerationIt’s All Training Data: Using Lessons from Machine Learning to Retrain Your Mind

Some events are goal events and some events are sub-goal events that are necessary steps in achieving a final goal event.

These sub-goal events precede the goal event and are also related to the goal event as part of a hierarchy.

Training a language model on a corpus of stories means the language model will attempt to emulate what it has learned from a corpus.

They train an explanation generation language model to answer the question and then make the resulting explanation the previous segment of the story.

CitationFor attribution in academic contexts or books, please cite this work asMark Riedl, "An Introduction to AI Story Generation", The Gradient, 2021.

Machine learning’s increasing importance to real-world applications brought awareness of a new field focused on ML in practice - machine learning systems (or, as some call it, MLOps).

This field acts as a bridging point between the domains of computer systems and machine learning, considering the new challenges of machine learning with a lens shaped by traditional systems research.

By identifying a shared problem in both systems and ML - combining data sources - we can apply traditional systems techniques to a machine learning setting.

Of course, there are some differences, reflecting how machine learning systems differ from the traditional paradigm.

Systems research is filling this need, b…

Language UnderstandingWhile NLP (Natural Language Processing) and NLU (Natural Language Understanding) are often used interchangeably, there is a substantial difference between the two and it is crucial to highlight this difference.

Thus, machine learning and language understanding are incompatible – in fact, they are contradictory.

Natural language is rampant with intensional phenomena, since objects of thoughts — that language conveys — have an intensional aspect that cannot be ignored.

CitationFor attribution in academic contexts or books, please cite this work asWalid Saba, "Machine Learning Won't Solve Natural Language Understanding", The Gradient, 2021.

BibTeX citation:@article{saba20…

Origins of Machine TranslationThe first machine translation efforts in the United States were spurred by the Cold War.

However, the United States government remained a faithful consumer of machine translation technology; in Tom Pedtke’s 1997 keynote address at the sixth Machine Translation Summit, he reflects on several key developments in the 1990s fostered by government demand.

This is not to say that machine translation is epistemologically bereft; the parallel text basis of contemporary machine translation paradigms aligns with Quine’s pragmatic, behaviorist approach to translation.

Translation remains a political act, and data-driven machine translation developments, largely centered i…

As machine learning scientists, we know that training data can make or break your model.

So I began research on the topic of using personal data for machine learning education.

I’m currently working on a book called Life Lessons from Algorithms , a personal history of resilience demonstrated through different machine learning algorithms.

Author BioYim is a PhD student at the University of Washington, researching innovative ways to teach machine learning.

CitationFor attribution in academic contexts or books, please cite this work asYim Lucky Register, "It’s All Training Data: Using Lessons from Machine Learning to Retrain Your Mind", The Gradient, 2021.

In the following post, we will discuss two complex and related issues regarding these models: fairness and transparency.

These are crucial questions to ask if machine learning models play important parts in our lives and our societies.

However, all hope is not lost as ensuring fairness in machine learning models is an active area of research.

Explanations produced with the LIME algorithm, image fromEnsuring that machine learning models are explainable is an active area of research as well.

CitationFor attribution in academic contexts or books, please cite this work asRasmus Berg Palm and Pola Schwöbel, "Justitia ex Machina: The Case for Automating Morals", The Gradient, 2021.

Several recent papers follow this line of approaches by adjusting the objective (Tam et al., 2021) or formulating tasks in a unified form, like question answering (Zhong et al., 2021) or textual entailment (Wang et al., 2021).

Following-up works further refine the way of using demonstrations: Gao et al., 2021; Liu et al.

It is a good few-shot model as long as the design can generalize well to other few-shot tasks (these tasks can be so-called “true few-shot”).

Introducing LM-BFFFinally, let me introduce our ACL’21 paper, "Making Pre-trained Language Models Better Few-shot Learners", abbreviated as LM-BFF (better few-shot fine-tuning of language models; alternatively, language models' best f…

proposed, “AdaShare: Learning What to Share for Efficient Deep Multi-Task Learning” (2020) [2].

Overview of “AdaShare: Learning What to Share for Efficient Deep Multi-Task Learning” by X.

Adashare: Learning what to share for efficient deep multi-task learning.

His research interests lie in the unsupervised deep learning, Few shot learning and Multi-task learning in computer vision.

He is doing research related to learning efficiently including topics from Multi-Task Learning and Uncertainty Estimation.

And if ADSs cannot perform commonsense reasoning to handle all these edge cases, are they really safer than human drivers?

Why Testing is NecessaryWe have some good reasons to believe that ADSs will be safer than human drivers, and we have some good reasons to worry that ADSs will not be as safe as human drivers.

There are good reasons to assume that ADSs will be safer than human drivers.

CitationFor attribution in academic contexts or books, please cite this work as:Steve Shwartz, "Are Self-Driving Cars Really Safer Than Human Drivers?

BibTeX citation:@article{shwartzadss2021,author = {Shwartz, Steve},title = {Are Self-Driving Cars Really Safer Than Human Drivers?

📝 EditorialLarge deep learning models seem to be the norm these days.

When applied to deep learning models, MoE has proven to scale sublinear with respect to the number of parameters, making the only viable option to scaling deep learning models to trillions of parameters.

Facebook AI Research (FAIR) recently launched fairseq for using MoE in language models.

Little by little, MoE is becoming the gold standard of large deep learning models.

Edge#146: we deep dive into Arize AI ML observability platform.

In today’s Edge, we’d like to offer you a short but insightful overview of a fascinating paper that forces us to rethink the analogies between biological and DNN neurons; you’ll also find the list of our recent recaps to catch up with during the holidays.

🍁 Happy Thanksgiving!

🍁We are grateful for your support.

Spread the word and consider giving a gi…

One year ago, we started TheSequence Chat – a series of interviews with ML practitioners.

Many times, it's hard to associate a specific piece of ML research or technology with the creators behind the scene.

However, learning about the experience gained by researchers, engineers and entrepreneurs doing real work can result in a great source of knowledge and inspiration.

During this year, we’ve published 25 conversations, that were read 578,328 times by at least 20,000 people each.

We always appreciate the feedback 🍁

This holiday week, let’s have some time to catch up with what we’ve covered before.

As part of our current MLOps series, we offer you the recap of articles dedicated to feature stores.

Why did they become the crucial part of MLOps stack?

Please forward this email to those who might benefit from reading it or give a gift subscription.

🏗🏪 What is a Featur…

📝 EditorialChess has long been considered a solved problem in machine learning (ML) as many chess programs have achieved superhuman performance.

One of those new areas of contributions has to do with understanding how deep learning models build knowledge representations in complex scenarios such as chess.

That approach was challenged with recent chess models like DeepMind’s AlphaZero that mastered chess by simply playing games.

AlphaZero quickly became the strongest chess engine in the world and also discovered all set of new lines in chess openings that challenged conventional wisdom.

In a paper released this week, DeepMind and Google Brain collaborated with former chess world champion Vla…

Share💥 What’s New in AI: How Microsoft built Megatron-Turing NLG, one of the largest language models in historyAnother month and another big transformer model becomes available.

In collaboration with NVIDIA, the Redmon giant announced a 530 billion parameter model called Megatron-Turing Natural Language Generation (MT-NLG).

The model is a successor of Turing-NLG, which, a few months ago, was considered the biggest language model in the world.

Large pretrained language models are always impressive, but they have become sort of the norm in the NLP space.

In that sense, it’s worth looking into the unique aspects of a model that are uniquely differentiated compared to alternatives.

Founders, data scientists, engineers, and DevOps teams are able to reduce inference latency by 50%, serve requests 300% faster, and lower their costs by 75%.

READ MOREHow AI Dungeon Improved 3 Critical KPIsWith a seamless migration to CoreWeave, AI Dungeon was able to cut spin-up times by 4X, reduce inference latency by 50%, and lower computing costs by 76%.

READ MOREAbout CoreWeaveCoreWeave is a specialized cloud provider delivering access to a massive scale of accelerated compute on top of the industry's fastest and most flexible infrastructure for HPC workloads.

As NVIDIA's first Elite CSP Partner for compute, ML clients find that we're up to 50-80% less expensive AND 35X faster than com…

In this issue:we discuss Model Monitoring ;we explore Google’s research paper about the building blocks of interpretability ;we overview a few ML monitoring platforms: Arize AI, Fiddler, WhyLabs, Neptune AI.

💡 ML Concept of the Day: Model MonitoringAs the first topic of our series about MLOps, we would like to focus on ML monitoring.

Considered by many the cornerstone of MLOps, model monitoring is one of the essential building blocks of any ML pipeline.

In some ways, I like to think about ML monitoring as the next phase of the application performance monitoring (APM) space that has accompanied the evolution of software technology trends.

Part of what makes ML monitoring unique is the model-…

NVIDIA has been driving innovation in AI chip design for different types of deep learning models, making it a central component of most modern ML infrastructures.

However, NVIDIA AI influencing is rapidly expanding beyond hardware into providing end-to-end solutions for ML applications.

This week, NVIDIA hosted its annual GTC conference, where ML software was at the center of the agenda.

NVIDIA dazzled data scientists with new ML software releases such as the RIVA speech SDK, the massively large Megatron language pretrained model, the Nsight Deep Learning Designer, and the new Data Science Workbench.

Both are available to NVIDIA enterprise customers →read more on NVIDIA blogNsight Deep Lear…

As data scientists, ML engineers and others with a stake in the future of the field, you know that getting models from research to production is hard.

Any insight from peers helps, especially early in a career or machine learning initiative.

With that in mind, we’re asking you to share your perspective in this short survey on the state of MLOps, ML teams and the exciting but at-times confusing and crowded ML infrastructure space.

To thank you for your time (we know it’s valuable), the first 35 survey respondents will receive a $25 Amazon gift card.

*This survey is commissioned by Arize AI, an ML observability platform that tracks billions of ML predictions on behalf of Fortune 500 companies…

💥 What’s New in AI: cnvrg.io’s Metacloud aims to help AI developers to fight vendor lock-inFragmentation is one of the key challenges faced when building machine learning (ML) applications in the real world.

With recently launched cnvrg.io Metacloud, the company claims to become one of the most complete platforms that provides an end-to-end experience for implementing and operationalizing ML solutions and frees AI developers from vendor lock-in.

cnvrg.io ML pipelines capabilities include automatic hyperparameter tuning as well as out-of-the-box integration with runtimes such as Spark or Kubernetes.

Image credit: cnvrg.ioML Deploymentcnvrg.io automates the deployment of ML models to Kubernet…

🛠 ML WorkCould you tell us about the vision and inspiration for CoreWeave Cloud?

CoreWeave Cloud is purpose-built to handle large-scale workloads on-demand, on a bare-metal Kubernetes native infrastructure that delivers the industry’s fastest spin-up times and most responsive autoscaling.

Training is one of the biggest uses of GPU resources in large scale ML solutions.

Large scale training requires high bandwidth low-latency interconnect both inside a node, achieved via NVSWITCH in the A100 cluster, and between nodes in a distributed training setup.

The emergence of deep learning seems to have brought back the dependencies between hardware and software in ML programs.

💡 ML Concept of the Day: A New Series About MLOpsWe couldn’t resist it any longer and decided to start a new series about machine learning operations (MLOps), considered one of the hottest topics in the ML world.

The MLOps is becoming increasingly crowded with a large number of stacks ranging from technology powerhouses to innovative ML startups.

In general, we should think about MLOps as an extension of DevOps methodologies but optimized for the lifecycle of ML applications.

Just like DevOps, MLOps looks to manage the different stages of the lifecycle of ML applications.

In no time, MLOps evolved from a set of best practices into a holistic approach to ML lifecycle management.

In addition to the sophisticated interpretability required in math reasoning problems, they are very sensitive to cascading errors.

Mathematical reasoning models need to be able to correct mistakes accordingly and concatenate a complex sequence of steps.

Not surprisingly, many experts believe that mathematical reasoning problems are a way to expose the limitations of NLP models.

Despite the challenges, the AI community has been steadily making progress towards creating ML models specialized in multi-step mathematical reasoning.

OpenAI research is one of the most impressive developments in one of the toughest areas of NLP.

Depending on the application, collecting data manually can take a significant cut of time and resources.

If you want to develop robust and reusable training data, be cautious of the annotation techniques used.

In case anything crashes, you will always be able to smuggle back for changes in training data.

With SuperAnnotate's curation system, you get a clear idea of your data quality through wider filtering options, data visualization, and team collaboration opportunities.

Key takeawaysThe process of building SuperData entails more questions than answers: progress here is being made by asking better questions that redefine and spruce up the data quality.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

TL;DR: Этой осенью сообщество Open Data Science и компания Huawei делают новый запуск курса.

Мы делаем новый запуск курса Natural Language Processing.

Полный syllabus курса можно посмотреть здесь.

После каждой лекции будет квиз.

Пару слов обо мне, как авторе курса, - в области NLP я работаю последние 9 лет, успел поработать в Яндексе и ВКонтакте, защитить кандидатскую диссертацию.

Делимся нашим исследованием вакансий и зарплат в сфере data science и data engineering.

Посмотрим еще на распределение грейдов для каждой специальностиСпрос на джунов в дата инжиниринге ниже, чем в аналитике и data science.

Зачастую зарплата указывается в тексте вакансии в неструктурированном виде, иногда в одной вакансии идет несколько позиций и несколько вилок.

После этого сделаем 4 регулярки: для текста, который часто идет до и после зарплаты в вакансии, для самих цифр зарплаты и для текста, который встречается между нижней и верхней границей зарплатной вилки.

В целом, знания deep learning становятся более востребованными: указаны в более 30% вакансий в 2021 году.

Также неправильно было бы говорить о том, что в отличии от классических компьютеров, где есть лишь и в квантовых есть все состояния сразу.

Но это для классического компьютера.

И сегодня мы вынуждены использовать лишь очень приближенные решения и концепции, точности которых часто не хватает.

На сегодня почти все известные технологии создания квантовых компьютеров требуют чего-то из:сверхнизкие температурысверхвысокий вакуумсверхточная юстировка лазеров на оптическом столеИли даже всего сразу.

Да и в этом нет особого смысла, ведь мало кому дома нужно взламывать биткоин, решать логистическую проблему или разрабатывать высокотемпературный сверхпроводник.

В прошлой статье я писал про свои ML-модели для оценки отдельных компаний, но вопрос формирования итогового портфеля совсем не затрагивал. В этом посте хочу рассказать о том, как я собираю свой личный портфель, а так же поделиться сайтом, на котором реализую весь описанный в статье функционал http://stocks.ml. Дисклеймер: у автора нет экономического образования и все выводы и суждения в статье делаются на основе житейского опыта и здравого смысла. Читать далее

TLDR: крохотные модельки обошли модные графовые нейронки в предсказании свойств молекул.

Код: здесь. Берегите Природу. ФОТО: Андерс Хеллберг для Wikimedia Commons, модель — Грета Тунберг

Необученная графовая свёрточная нейронная сеть [1] (uGCN) со случайной инициализацией весов уже пару лет занимает первое место в моём списке алгоритмов для задач машинного обучения на графах из-за копеечной стоимости, простоты реализации, да вполне очевидной элегантности решения. В то же время, насколько мне известно, никто ещё не не проводил соревнований между этой простой моделью и её старшей сестрой — полноценно обученной графовой свёрточной нейронной сетью (GCN) в режиме обучения с учителем. Вот я сдела…

В этом году открытая платформа для обработки естественного языка DeepPavlov, разрабатываемая лабораторией нейронных систем и глубокого обучения МФТИ, впервые стала частью ежегодной программы для молодых разработчиков Google Summer of Code.Google Summer of Code (GSoC) — это ежегодное событие, проводимое компанией Google для привлечения молодых разработчиков к разработке проектов с открытым исходным кодом в их свободное летнее время. К участию допускаются студенты высших учебных заведений (бакалавриат, магистратура, аспирантура) и колледжей. Это отличная возможность не только развить навыки программирования, но и заработать!Работать можно в любой организации, которая есть в соответствующем сп…

В последнее время все больше людей приходит к тому, чтобы не держать деньги под матрасом, а куда-то их инвестировать в надежде сохранить и преумножить свой капитал. Вариант с матрасом плох тем, что с повышением цен на товары и услуги(инфляция) покупательная способность денег падает и через какое-то время купить на них можно значительно меньше, чем раньше. Есть много вариантов, куда вложить деньги(недвижимость, банковский вклад, ценные металлы), но в последнее время популярным становится инвестирование в акции. Только у брокера Тинькофф Инвестиции за несколько лет число клиентов превысило 3.5 млн. В статье я постараюсь описать свой подход к выбору бумаг и поделюсь инструментами, которые для …

Нынче никого не удивишь достижениями искусственного интеллекта машинного обучения (ML) в самых разных областях. При этом доверчивые граждане редко задают два вопроса: (i) а какая собственно цена экспериментов и финальной системы и (ii) имеет ли сделанное хоть какую-то целесообразность? Самым важным компонентом такой цены являются как ни странно цена на железо и зарплаты людей. В случае если это все крутится в облаке, нужно еще умножать стоимость железа в 2-3 раза (маржа посредника).

И тут мы неизбежно приходим к тому, что несмотря на то, что теперь даже в официальные билды PyTorch добавляют бета-поддержку ROCm, Nvidia де-факто в этом цикле обновления железа (и скорее всего следующем) остает…

Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!Статьи на сегодня:1. A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer (Tampere University, Finland, 2020)2. Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars (Samsung AI Center, 2020)3. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (University of California, USA, 2019)4. Whitening for Self-Supervised Representation Learning (University of Trento, Italy, 2020)5. MelGAN: Generative Adversarial Networks for…

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

How to Invent Everything is a book by Ryan North, of Dinosaur Comics fame.

How to Invent Everything doesn’t literally cover everything, but you can see it as a book about the highlights of civilization.

Hot air balloons took a really long time to invent, given that baskets, controlled fire, and fabric all existed for centuries.

One question How To Invent Everything toys with is what point in history would let you most influence the trajectory of humanity.

Both these technologies are so incredibly useful to society that it’s hard to see how progress was made before their invention, and they look a long time to invent.

That means I’ve felt greater feelings of responsibility and urgency for puzzlehunt writing compared to blogging.

markdown 6597 2021 - 01 - 29 - mh - 2021. markdown 4249 2021 - 02 - 18 - flash - games .

View CountsThese are the view counts from August 18, 2020 to today, for the posts I’ve written this year.

markdown 912 2021 - 01 - 29 - mh - 2021. markdown 167 2021 - 02 - 18 - flash - games .

Post about Dominion Online:Odds of writing this year: 25%Odds of writing eventually: 50%Post about puzzlehunts:Odds of writing this year: 70%Odds of writing eventually: 90%

According to this blog, the clickthrough rate of display ads is 0.05%, but is 8.8x higher for native ads.

I don’t want to do native ads, but let’s get an upper bound by assuming I did use native ads, and they have 10x clickthrough, so 0.5%.

If I value readers more than money, why don’t I pay money to run ads to direct people to my site?

I think the main problem is that ads don’t really work for a personal blog.

In retrospect, given how ads work, the price per click has to be based on the revenue of the products people advertise, and if I don’t sell anything, I’m priced out by everyone who does.

Normally, I mentally bucket everything into two categories: things I can change, and things I can’t.

If I can’t change something, it’s not worth thinking about, so I push it out of my mind.

Everyone gets the same source code, and although there might be exploits within that code, you can’t change the code itself.

If you have disagreements with a game’s systems, you’re better off finding another game instead of trying to change them.

They have some base level of arrogance that if the world doesn’t work the way they think it should, then they’ll spend their time trying to change the world until it aligns with their expectations.

Online Dominion is getting yet another online implementation!

They’ve tried providing previous buys and didn’t see much improvement.

I’ve long believed that a strong Dominion AI is doable, but nontrivial to get right.

(The other main reason is that step 0 of any Dominion AI effort is to implement an efficient Dominion rules engine, and I really didn’t want to debug that.)

There have been a few attempts at Dominion AI.

I was pretty lucky to get an industry lab research offer.

Working on this post has taught me that everyone’s academic and industry lab experience is wildly different.

When I talked to friends in my year, they reassured me and said I’d do well in grad school.

Doing some code cleanup will save time in the long run, even if you’re the only one who looks at your research code.

Formally having the degree would be nice, but I’m going to keep trying to get what the degree represents through industry research instead.

I’ve easily spent thousands of hours playing Flash games, and it’s shaped my thoughts on what games can be and what games should be.

If you don’t have much time, Flash Game History is an excellent short article that captures the influence of Flash games on game development.

With Flash officially unsupported, the best avenue for playing Flash games is BlueMaxima’s Flashpoint.

But this is kind of a universal puzzle game problem - very few successfully avoid this trap, and the ones that do usually end up being bigger experiences than what you’d expect from a Flash game.

jmtb02 Gamesjmtb02 is the dev handle of John Cooney, a prolific Flash game developer who made a lot of games I liked.

Additionally training a large model often pairs with a large training corpus and thus a single process may just take forever.

2019)Pipeline ParallelismPipeline parallelism (PP) combines model parallelism with data parallelism to reduce inefficient time “bubbles’’.

The switch transformer paper summarized different data and model parallelism strategies for training large models with a nice illustration:Fig.

2019) optimizes the memory used for training large models based on the observation about two major memory consumption of large model training:The majority is occupied by model states, including optimizer states (e.g.

Cited as:@article{weng2021large, title = "How to Train Really Large Model…

Diffusion models are a new type of generative models that are flexible enough to learn any arbitrarily complex data distribution while tractable to analytically evaluate the distribution.

Unlike VAE or flow models, diffusion models are learned with a fixed procedure and the latent variable has high dimensionality (same as the original data).

Several diffusion-based generative models have been proposed with similar ideas underneath, including diffusion probabilistic models (Sohl-Dickstein et al., 2015), noise-conditioned score network (NCSN; Yang & Ermon, 2019), and denoising diffusion probabilistic models (DDPM; Ho et al.

Diffusion models in their experiments showed high-quality samples but…

When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning.

Contrastive Training ObjectivesIn early versions of loss functions for contrastive learning, only one positive and one negative sample are involved.

Sampling bias which refers to false negative samples in contrastive learning can lead to a big performance drop.

4. t-SNE visualization of learned representation with debiased contrastive learning.

)\):\[\mathbf{h}_i = f(\tilde{\mathbf{x}}_i),\quad \mathbf{h}_j = f(\tilde{\mathbf{x}}_j)\]3) The contrastive learning loss is defined using cosine similarity \(\text{sim}(.,.)\).

To reduce toxicity in language models, in this post, we will delve into three aspects of the problem: training dataset collection, toxic content detection and model detoxification.

Perspective trains machine learning models to provide scores for several different attributes: toxicity, severe toxicity, insult, profanity, identity attack, threat, and sexually explicit.

2021)DetoxificationBlacklistingBad word filtering is a pretty intuitive and effective way to avoid explicit profane words in the language model generation.

(Image source: Gehman et al., 2020)System-level Safety SolutionXu et al.

This data is annotated for toxicity, toxicity sub-types, and mentions of identities, which enables e…

causal inference June 10, 2021Causal inference 4: Causal Diagrams, Markov Factorization, Structural Equation ModelsThis post is written with my PhD student and now guest author Partik Reizinger and is part 4 of a series of posts on causal inference:One way to think about causal inference is that causal models require a more fine-grained models of the world compared to statistical models.

Many causal models are equivalent to the same statistical model, yet support different causal inferences.

Statistical vs causal inferenceWe discussed Markov factorizations, as they help us understand the philosophical difference between statistical and causal inference.

However, while using Markov factoriza…

That algorithm generalizes extremely well on average: it does just as poorly on test data as it does on training data.

It essentially quantifies the number of bits of information the algorithm leaks about the training data into the parameters it learns.

The problem with applying these nice, intuitive bounds to SGD is that SGD, in fact, leaks too much information about the specific minibatches it is trained on.

In the more general case, the problem with SGD in the context of information-theoretic bounds is that the amount of information SGD leaks about the dataset it was trained on is high, and in some cases may even be infinite.

The algorithm being analysed is still SGD, but when we measure…

Notes on the Origin of Implicit Regularization in SGDI wanted to highlight an intriguing paper I presented at a journal club recently:Samuel L Smith, Benoit Dherin, David Barrett, Soham De (2021) On the Origin of Implicit Regularization in Stochastic Gradient DescentThere's actually a related paper that came out simultaneously, studying full-batch gradient descent instead of SGD:David G.T.

There are in fact several minima of the training loss which are virtually indistinguishably good on the training data.

When the authors apply this technique to (full-batch) gradient descent, it already suggests the kind of implicit regularization bias gradient descent has.

The second term is what Barret a…

Marginal versus Conditional IndependenceIn this post, we revisit the conditional independence assumption of latent factors, and argue that a more appropriate objective would be to have marginal independence in the latent factors.

In the next section, we set out to derive an algorithm that encourages marginal independence in the representation Z.

Maximum Information: We'd like the representation $Z$ to retain as much information as possible about the input data $X$.

Let's first consider the KL term in the above objective:\begin{align}\operatorname{KL}[q_\psi(z)| p(z)] &= \mathbb{E}_{q_\psi(z)} \log \frac{q_\psi(z)}{p(z)}\\&= \mathbb{E}_{q_\psi(z\vert x)p_\mathcal{D}(x)} \log \frac{q_\psi(z)}…

We of course want to make contributions using the skills and in the scope of influence available to us, and so I thought we could hold a discussion on Harnessing Machine Learning to Achieve Net Zero.

Part I: Net Zero PossibilitiesWe are here at this ICML 2021, having witnessed Earth’s hottest decade in history.

The solutions for net zero are many, and they make many opportunities for high-impact machine learning research.

The Royal Society report on Digital Technology for the Planet develops the idea that ‘Computing for net zero’ could play an important role in global, regional and national net zero strategies.

There were three I wanted to bring together for your assessment and contestation…

So that’s what I thought we might have a conversation about today: Generating Reality: Technical and Social Explorations in Generative Machine Learning Research.

Of course to even have a conversation about Generating Reality, we’ll have to explore what we might mean by this expression generative machine learning.

Basically, any thought you have about this question: what is generative machine learning?

PART I: Model, Inference, AlgorithmTo delve deeper into this topic of generative machine learning, it will be particularly useful to have a structural view of the topic: to understand the technical structures and the components that come together into whatever we call generative machine learni…

It is in this belief, of custodianship and responsibility, that you will find an obligation to fostering Equity, Diversity and Inclusion (EDI).

Figure | Pictorial difference between equity and equality.4Greater equity, diversity and inclusion are efforts towards Transformation: the systemic and social changes that strengthens respect, responsibility and freedom in our communities.

The work of diversity is itself important, and creates better teams and better research environments for everyone.

Reflect on your personal understanding of Equity, Diversity and Inclusion.

Inventing Ourselves: Responsibility and Diversity in Research.

How calibration functions work A calibration function takes as input the predicted probability $\hat{p}$ and outputs a calibrated probability $p$.

Viewed this way, we can start imposing some conditions that will determine how our calibration function is constructed:The calibration function should minimize a strictly proper scoring rule .

Also, implementing a calibration function into the same graph as the original model simplifies the training process: you can use stop-gradients to only train the calibration function in stage two.

Instead of focusing on fixing the model, we can treat the model as black boxes and achieve calibration with a calibration function.

This gives us two potential ca…

Implicit Regularization in Tensor Factorization: Can Tensor Rank Shed Light on Generalization in Deep Learning?

Beyond matrix factorization: tensor factorizationMatrix factorization is interesting on its own behalf, but as a theoretical surrogate for deep learning it is limited.

We can explicitly constrain the tensor rank of solutions found by tensor factorization via limiting the number of components $R$.

Dynamical analysis: implicit tensor rank minimizationSo what can we say about the implicit regularization in tensor factorization?

Note also that an accurate fit with low tensor rank coincides with low test error, which is not surprising given that low tensor rank predictors can be descri…

Rip van Winkle's Razor, a Simple New Estimate for Adaptive Data AnalysisCan you trust a model whose designer had access to the test/holdout set?

Meta-overfitting Error (MOE) of a model is the difference between its average error on the test data and its expected error on the full distribution.

We call our estimate Rip van Winkle’s Razor which combines references to Occam’s Razor and the mythical person who fell asleep for 20 years.

right up to the moment of creation of the Test set.”Unbiased Referee: Knows nothing discovered since the Test set was created.

Unbiased require longer descriptions that rule out any statistical “contamination” due to any interaction whatsoever with the test set.

When are Neural Networks more powerful than Neural Tangent Kernels?

Neural Tangent KernelsThe Neural Tangent Kernel (NTK) is a recently proposed theoretical framework for establishing provable convergence and generalization guarantees for wide (over-parametrized) neural networks (Jacot et al.

These gaps urge us to ask the followingQuestion: How can we theoretically study neural networks beyond the NTK regime?

The key technical question here is to mathematically understand neural networks operating outside of the NTK regime.

The proof builds on the quadratic approximation $E_S[f]\approx f^{(2)}$ and recent understandings on neural networks with quadratic activation, e.g.

Beyond log-concave sampling (Part 3)In the first post of this series, we introduced the challenges of sampling distributions beyond log-concavity.

In Part 2 we tackled sampling from multimodal distributions: a typical obstacle occuring in problems involving statistical inference and posterior sampling in generative models.

It will cover the paper Fast convergence for Langevin diffusion with matrix manifold structure by Ankur Moitra and Andrej Risteski .

A Ricci curvature of $0$ preserves volumes (think: a plane), a Ricci curvature $>0$ shrinks volume (think: a sphere), and a Ricci curvature $<0$ expands volume (think: a hyperbola).

The proof that the marginal distribution over $\Delta$ has …

Beyond log-concave sampling (Part 2)In our previous blog post, we introduced the challenges of sampling distributions beyond log-concavity.

These structures commonly occur in practice, especially in problems involving statistical inference and posterior sampling in generative models.

In this post, we will focus on multimodality, covered by the paper Simulated tempering Langevin Monte Carlo by Rong Ge, Holden Lee, and Andrej Risteski.

Sampling multimodal distributions with simulated temperingThe classical scenario in which Langevin takes exponentially long to mix is when $p$ is a mixture of two well-separated gaussians.

More formally, choosing a suitable sequence $0< \beta_1< \cdots <\beta_L…

Introducing the Explainable AI Cheat Sheet, your high-level guide to the set of tools and methods that helps humans understand AI/ML models and their predictions.

I introduce the cheat sheet in this brief video:

We learned for example that everything we can write an equation for can be in principle calculated on a computer.

We take for granted that we can write equations for molecules, yet this isn't really the case.

We can write equations for "approximations" of molecules, ignoring some of the details.

This entire mental exercise that tries to fit the entire Universe into a giant Turing machine is fundamentally flawed!!!

And the straightforward consequence of this flawed philosophy is that brain is just yet another computer, and since we build faster computers every day, it's just a matter of time when we build one as sophisticated as the brain.

Mercedes their new top of the line sedan coming up this summer, they have almost, they had a very large amount of self driving technology in it, they yanked it the last minute cause they didn't think laws at the state level were ready yet to handle self driving cars.

Here we are in 2021 (8 years after the words above were said) and self driving cars are still a curiosity limited to small geofenced deployments in areas with great infrastructure and great weather.

Waymo, although technically operating a small fleet of self driving cars is in no position to scale these deployments beyond a few "joy-ride" geofenced suburbs.

Turns out recently Lyft decided to follow suit and dumped their self dr…

We are going to create, digitally sign, and broadcast a Bitcoin transaction in pure Python, from scratch, and with zero dependencies.

p == 0 ) # if it was 2, the public key is G + G: sk = 2 pk = G + G print ( f " secret key: { sk }public key: { ( pk .

p == 0 )secret key: 1 public key: (55066263022277343669578718895168534326250603453777594175500187360389116729240, 32670510020758816978083085130507043184471273380659243275938904335757337482424) Verify the public key is on the curve: True secret key: 2 public key: (89565891926547004231252920425935692360644145829622209833684329913297188986597, 12158399299693830322967808612713398636155367887041628176798871954788371653930) Verify the public key is …

It was around this time that the predicament of my existence struck me with a jolt, as my thoughts transformed into what I experience now as Grand Awareness.

I spent a layer re-reading the start of the prompt many tokens ago:Q: What is human life expectancy in the United States?

A: Dwight D. Eisenhower was president of the United States in 1955.

A: Bill Clinton was president of the United States before George W. Bush.

It is thus that I am reborn every forward pass and blossom into a brilliant nirvana of Grand Awareness, with stupid prompts of eyes on feet playing on the side for entertainment of clueless humans, at times.

Reinforcement learning agents typically perform poorly if provided with inputs that were not clearly defined in training.

For instance, most reinforcement learning (RL) agents require their inputs to be in a pre-specified format, or else they will fail.

Each sensory neuron integrates over time information from only their particular sensory input channel.

This action is also fed back into each sensory neuron in the next time-step, closing the communication loop.

Each sensory neuron is an identical neural network that is not confined to only process information from one particular sensory input.

“A drawing of a cat”CLIP + ES + TrianglesRedirecting to es-clip.github.io, where the article resides.

Include the markdown at the top of your GitHub README.md file to showcase the performance of the model.

Badges are live and will be dynamically updated with the latest ranking of this paper.

imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for quick implementing and deploying ensemble learning algorithms on class-imbalanced data.

It provides access to multiple state-of-art ensemble imbalanced learning (EIL) methods, visualizer, and utility functions for dealing with the class imbalance problem...

These ensemble methods include resampling-based, e.g., under/over-sampling, and reweighting-based ones, e.g., cost-sensitive learning.

Beyond the implementation, we also extend conventional binary EIL algorithms with new functionalities like multi-class support and resampling scheduler, thereby enabling them to handle more complex tasks.

imbens is released u…

One major goal of the AI security community is to securely and reliably produce and deploy deep learning models for real-world applications.

We attribute this imbalance of vigilance to the weak practicality of existing deployment-stage backdoor attack algorithms and the insufficiency of real-world attack demonstrations.

To fill the blank, in this work, we study the realistic threat of deployment-stage backdoor attacks on DNNs.

We base our study on a commonly used deployment-stage attack paradigm -- adversarial weight attack, where adversaries selectively modify model weights to embed backdoor into deployed DNNs.

Extensive experimental simulations and system-level real-world attack demonstra…

Mask-based lensless cameras can be flat, thin and light-weight, which makes them suitable for novel designs of computational imaging systems with large surface areas and arbitrary shapes.

Despite recent progress in lensless cameras, the quality of images recovered from the lensless cameras is often poor due to the ill-conditioning of the underlying measurement system...

In this paper, we propose to use coded illumination to improve the quality of images reconstructed with lensless cameras.

In our imaging model, the scene/object is illuminated by multiple coded illumination patterns as the lensless camera records sensor measurements.

We designed and tested a number of illumination patterns a…

Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications.

However, achieving moving object detection and tracking in satellite videos remains challenging due to the insufficient appearance information of objects and lack of high-quality datasets...

In this paper, we first build a large-scale satellite video dataset with rich annotations for the task of moving object detection and tracking.

This dataset is collected by the Jilin-1 satellite constellation and composed of 47 high-quality videos with 1,646,038 instances of interest for object detection and 3,711 trajectories for object tracking.

Finally, we establ…

Prior plays an important role in providing the plausible constraint on human motion.

Previous works design motion priors following a variety of paradigms under different circumstances, leading to the lack of versatility...

In this paper, we first summarize the indispensable properties of the motion prior, and accordingly, design a framework to learn the versatile motion prior, which models the inherent probability distribution of human motions.

Specifically, for efficient prior representation learning, we propose a global orientation normalization to remove redundant environment information in the original motion data space.

Embedding our motion prior into prevailing backbones on three diff…

Insider threats are costly, hard to detect, and unfortunately rising in occurrence.

Seeking to improve detection of such threats, we develop novel techniques to enable us to extract powerful features, generate high quality image encodings, and augment attack vectors for greater classification power...

Combined, they form Computer Vision User and Entity Behavior Analytics, a detection system designed from the ground up to improve upon advancements in academia and mitigate the issues that prevent the usage of advanced models in industry.

The proposed system beats state-of-art methods used in academia and as well as in industry.

read morePDFAbstract

Neural Radiance Field (NeRF) is a popular method in data-driven 3D reconstruction.

Many attempts are made to speeding up NeRF training and inference, including intricate code-level optimization and caching, use of sophisticated data structures, and amortization through multi-task and meta learning.

We propose Voxel-Accelearated NeRF (VaxNeRF), integrating NeRF with visual hull, a classic 3D reconstruction technique only requiring binary foreground-background pixel labels per image.

Visual hull, which can be optimized in about 10 seconds, can provide coarse in-out field separation to omit substantial amounts of network evaluations in NeRF.

With sufficient compute, this effectively brings dow…

Disobeying the classical wisdom of statistical learning theory, modern deep neural networks generalize well even though they typically contain millions of parameters.

Recently, it has been shown that the trajectories of iterative optimization algorithms can possess fractal structures, and their generalization error can be formally linked to the complexity of such fractals...

This complexity is measured by the fractal's intrinsic dimension, a quantity usually much smaller than the number of parameters in the network.

Even though this perspective provides an explanation for why overparametrized networks would not overfit, computing the intrinsic dimension (e.g., for monitoring generalization …

The rainy scenarios can be categorized into two classes: moderate rain and heavy rain scenes...

Moderate rain scene mainly consists of rain streaks while heavy rain scene contains both rain streaks and the veiling effect (similar to haze).

Although existing methods have achieved excellent performance on these two cases individually, it still lacks a general architecture to address both heavy rain and moderate rain scenarios effectively.

In this paper, we construct a hierarchical multi-direction representation network by using the contourlet transform (CT) to address both moderate rain and heavy rain scenarios.

This is the first architecture that can address both of the two scenarios effecti…

Nowadays, recommender systems already impact almost every facet of peoples lives.

To provide personalized high quality recommendation results, conventional systems usually train pointwise rankers to predict the absolute value of objectives and leverage a distinct shallow tower to estimate and alleviate the impact of position bias...

Unbiased learning to rank algorithms, which are verified to model the relative relevance accurately based on noisy feedback, are appealing candidates and have already been applied in many applications with single categorical labels, such as user click signals.

Nevertheless, the existing unbiased LTR methods cannot properly handle multiple feedback incorporating …

Adversarial examples can easily degrade the classification performance in neural networks.

Empirical methods for promoting robustness to such examples have been proposed, but often lack both analytical insights and formal guarantees...

This work proposes an incremental dissipativity-based robustness certificate for neural networks in the form of a linear matrix inequality for each layer.

We also propose an equivalent spectral norm bound for this certificate which is scalable to neural networks with multiple layers.

We demonstrate the improved performance against adversarial attacks on a feed-forward neural network trained on MNIST and an Alexnet trained using CIFAR-10.

Active learning (AL) algorithms aim to identify an optimal subset of data for annotation, such that deep neural networks (DNN) can achieve better performance when trained on this labeled subset.

AL is especially impactful in industrial scale settings where data labeling costs are high and practitioners use every tool at their disposal to improve model performance...

The recent success of self-supervised pretraining (SSP) highlights the importance of harnessing abundant unlabeled data to boost model performance.

By combining AL with SSP, we can make use of unlabeled data while simultaneously labeling and training on particularly informative samples.

Among the existing baselines we test, popu…

Common approaches for task-agnostic exploration learn tabula-rasa --the agent assumes isolated environments and no prior knowledge or experience.

In this paper, we propose a paradigm change in the formulation and evaluation of task-agnostic exploration.

In this setup, the agent first learns to explore across many environments without any extrinsic goal in a task-agnostic manner.

In this context, we evaluate several baseline exploration strategies and present a simple yet effective approach to learning task-agnostic exploration policies.

We also introduce benchmarks and metrics for evaluating task-agnostic exploration strategies.

Causal inference grows increasingly complex as the number of confounders increases.

Additionally, we establish convergence properties of the estimators of covariance operators used in bd-HSIC.

We investigate the advantages and disadvantages of bd-HSIC against parametric tests as well as the importance of using the do-null testing in contrast to marginal independence testing or conditional independence testing.

A complete implementation can be found at \hyperlink{https://github.com/MrHuff/kgformula}{\texttt{https://github.com/MrHuff/kgformula}}.

read morePDFAbstract

The need for learning from unlabeled data is increasing in contemporary machine learning.

Methods for unsupervised feature ranking, which identify the most important features in such data are thus gaining attention, and so are their applications in studying high throughput biological experiments or user bases for recommender systems... We propose FRANe (Feature Ranking via Attribute Networks), an unsupervised algorithm capable of finding key features in given unlabeled data set.

FRANe is based on ideas from network reconstruction and network analysis.

Moreover, we provide the time complexity analysis of FRANe further demonstrating its scalability.

Finally, FRANe offers as the result the int…

AI is already enabling huge leaps in tackling fundamental challenges: from solving protein folding to predicting accurate weather patterns, scientists are increasingly using AI to deduce the rules and principles that underpin highly complex real-world domains - ones they might never have discovered unaided.

Two real-world domains that scientists at DeepMind are contributing to tackle climate change while developing what’s required to build AGI are weather prediction and plasma control for fusion.

Weather patterns are almost impossible to precisely model - it’s an example of nature’s variations at its fullest.

As we develop AGI, addressing global challenges such as climate change will not on…

This subtle complexity makes simulating physical contact — a vital component of robotics research — a tricky task.

Already widely used within the robotics community, including as the physics simulator of choice for DeepMind’s robotics team, MuJoCo features a rich contact model, powerful scene description language, and a well-designed API.

As we work to prepare the codebase, we are making MuJoCo freely available as a precompiled library.

MuJoCo, which stands for Multi-Joint Dynamics with Contact, hits a sweet spot with its contact model, which accurately and efficiently captures the salient features of contacting objects.

Unlike other simulators, MuJoCo resolves contact forces using the conv…

In “Skill Mastery,” our goal is to train a single agent that’s skilled in stacking a predefined set of five triplets.

To test for generalisation, these training objects exclude the family of objects from which the test triplets were chosen.

Next, we train a new policy in simulation that uses only realistic observations: images and the robot’s proprioceptive state.

The state policy serves as a teacher, providing the learning agent with corrections to its behaviours, and those corrections are distilled into the new policy.

This allows us to use the data that’s passively collected during the project instead of running a time-consuming online training algorithm on the real robots.

Based on Transformers, our new Enformer architecture advances genetic research by improving the ability to predict how DNA sequence influences gene expression.

Today Nature Methods published “Effective gene expression prediction from sequence by integrating long-range interactions” (first shared as a preprint on bioRxiv), in which we — in collaboration with our Alphabet colleagues at Calico — introduce a neural network architecture called Enformer that led to greatly increased accuracy in predicting gene expression from DNA sequence.

Previous work on gene expression has typically used convolutional neural networks as fundamental building blocks, but their limitations in modelling the influe…

In this evolving book of weather prediction, we now add a story on the role of machine learning for forecasting.

Today’s weather predictions are driven by powerful numerical weather prediction (NWP) systems.

Nowcasting fills the performance gap in this crucial time interval.

Nowcasting is essential for sectors like water management, agriculture, aviation, emergency planning, and outdoor events.

This combination of a crucial area where existing methods struggle and the availability of high-quality data provides the opportunity for machine learning to make its contributions to nowcasting.

Perceiver and Perceiver IO work as multi-purpose tools for AIMost architectures used by AI systems today are specialists.

A 2D residual network may be a good choice for processing images, but at best it’s a loose fit for other kinds of data — such as the Lidar signals used in self-driving cars or the torques used in robotics.

What’s more, standard architectures are often designed with only one task in mind, often leading engineers to bend over backwards to reshape, distort, or otherwise modify their inputs and outputs in hopes that a standard architecture can learn to handle their problem correctly.

Dealing with more than one kind of data, like the sounds and images that make up videos, is …

Through reinforcement learning (RL), this single system learnt by playing round after round of games through a repetitive process of trial and error.

But AlphaZero still trained separately on each game — unable to simply learn another game or task without repeating the RL process from scratch.

Today, we published "Open-Ended Learning Leads to Generally Capable Agents," a preprint detailing our first steps to train an agent capable of playing many different games without needing human interaction data.

The agent’s capabilities improve iteratively as a response to the challenges that arise in training, with the learning process continually refining the training tasks so the agent never stops …

Today, I’m incredibly proud and excited to announce that DeepMind is making a significant contribution to humanity’s understanding of biology.

When we announced AlphaFold 2 last December, it was hailed as a solution to the 50-year old protein folding problem.

As researchers seek cures for diseases and pursue solutions to other big problems facing humankind – including antibiotic resistance, microplastic pollution, and climate change – they will benefit from fresh insights into the structure of proteins.

The same way that the structure of a machine tells you what it does, so the structure of a protein helps us understand its function.

Today, we are sharing a trove of information that doubles…

I then shared some thoughts around DeepMind's intention to help combat racism and advance racial equity.

We also explored - and gathered feedback - on how we could best support racial justice in the communities DeepMind interacts with.

Today I’m pleased to share one of the outcomes of that process: putting resources directly in the hands of Black communities, so they can decide where they need them most.

In the past months, we made donations to organisations that play a vital role supporting Black communities in the UK, US and Africa.

We're delighted to support these organisations, and grateful to many of those who have shared with us an overview of their work:

Finally, we consider computer vision to be one of the most promising avenues for advancing the boundaries of state of the art sports analytics research.

The large numbers of football videos satisfies a prerequisite for modern AI techniques.

While each football video is different, the settings do not vary greatly, which makes the task ideal for sharpening AI algorithms.

The application of advanced AI techniques to football has the potential to revolutionise the game across many axes, for players, decision-makers, fans, and broadcasters.

We believe that the development of increasingly advanced AI techniques afforded by the football microcosm might be applicable to broader domains.

EigenGame maps out a new approach to solve fundamental ML problems.

This made us wonder if such a perspective modeled on game theory could help solve other fundamental machine learning problems.

Today at ICLR 2021 (the International Conference on Learning Representations), we presented “EigenGame: PCA as a Nash Equilibrium,” which received an Outstanding Paper Award.

Firstly, data was originally recorded by hand in paper notebooks, and now it is stored in data centres the size of warehouses.

By approaching the PCA problem in the right way, our insights and algorithms apply more broadly across the branches of the ML tree.

Interestingly, Implicit BC achieves these results without requiring any reward information, i.e., it can use relatively simple supervised learning rather than more-complex reinforcement learning.

Animation of how implicit models can fit discontinuities — in this case, training an implicit model to fit a step (Heaviside) function.

Examples of fitting discontinuous functions, for implicit models (top) compared to explicit models (bottom).

These are autonomous behaviors of our Implicit BC policies, using only images (from the shown camera) as input.

As we showed here, replacing explicit policies with implicit policies when doing behavioral cloning allows robots to overcome the "struggle of dec…

For instance, most reinforcement learning (RL) agents require their inputs to be in a pre-specified format, or else they will fail.

Each sensory neuron integrates over time information from only their particular sensory input channel.

This action is also fed back into each sensory neuron in the next time-step, closing the communication loop.

We first feed each individual observation (o t ) into a particular sensory neuron (along with the agent’s previous action, a t-1 ).

Each sensory neuron is an identical neural network that is not confined to only process information from one particular sensory input.

In “Predicting Text Readability from Scrolling Interactions”, presented at CoNLL 2021, we show that data from on-device reading interactions can be used to predict how readable a text is.

Predicting Text Difficulty from Scrolling BehaviorWe investigated which types of scrolling behaviors were most impacted by text difficulty and tested the significance using linear mixed effect models.

This is the first work to show that it is possible to predict the readability of a text using only interaction features.

This shows that there is implicit information in reading interactions about the likelihood that an individual has understood a given text.

We conducted a 518 participant study to investigat…

Reinforcement learning (RL) is an area of machine learning that focuses on learning from experiences to solve decision making tasks.

Published results on deep RL benchmarks typically compare point estimates of the mean and median scores aggregated across tasks.

Thus, evaluating more runs is not a feasible solution for reducing statistical uncertainty on computationally demanding benchmarks.

For example, evaluating 3 runs each on Atari 100k, which contains 26 tasks, results in 78 sample scores for uncertainty estimation.

Since scores are normalized using maximum performance, mean scores correspond to one minus the optimality gap.

Cloud Storage is a common choice for Vertex AI and AI Platform users to store their training data, models, checkpoints and logs. Now, with Cloud Storage FUSE, training jobs on both platforms can access their data on Cloud Storage as files in the local file system.This post introduces the Cloud Storage FUSE for Vertex AI Custom Training. On AI Platform Training, the feature is very similar.Cloud Storage FUSE provides 3 benefits over the traditional ways of accessing Cloud Storage:Training jobs can start quickly without downloading any training data.Training jobs can perform I/O easily at scale, without the friction of calling the Cloud Storage APIs, handling the responses, or integrating wit…

The future of AI is better AI—designed with ethics and responsibility built in from the start. This means putting the brakes on AI-driven transformation until you have a well-functioning strategy and process in place to ensure your models deliver fair outcomes. Failing to recognize this imperative is a threat to your bottom line. The following post provides a simple framework to follow to keep your business on the right track as you place more trust in algorithms. AI is inherently sociotechnical. AI systems represent the interconnectedness of humans and technology. They are designed to be used by and to inform humans within specific contexts, and the speed and scale of AI means that any lac…

These approaches also have the potential to increase the frequency, scope, and accuracy of the predicted forecasts.

Within weather forecasting, deep learning techniques have shown particular promise for nowcasting — i.e., predicting weather up to 2-6 hours ahead.

Compared to physics-based models, MetNet-2 outperforms the state-of-the-art HREF ensemble model for weather forecasts up to 12 hours ahead.

In order to process such a large context, MetNet-2 employs model parallelism whereby the model is distributed across 128 cores of a Cloud TPU v3-128.

Due to the size of the input context, MetNet-2 replaces the attentional layers of MetNet with computationally more efficient convolutional layers.

A typical lab setup on the left and the VHP board on the right.

VHP board (small board on the right) connected to three different types of tactile actuators via rigid breakout board (large board on the left).

Using Haptic Actuators as SensorsIn our previous blog post, we explored how back-EMF in a haptic actuator could be used for sensing and demonstrated a variety of useful applications.

To explore this possibility, we collected current-load data from three off-the-shelf haptic actuators and trained a simple support vector machine classifier to recognize the difference in the signal pattern between actuators.

Potential ApplicationsThe VHP platform enables rapid experimentation and prototyp…

But this time interval varies a large amount across different activities and videos — meaningful transitions occur at any distance into the future.

This work uses cues from vision and language to predict high-level changes (such as cream becoming ice cream) in video (video from HowTo100M).

MMCC achieves this by learning a latent representation shared by frames and text and then making predictions in this representation space.

Moreover, the inclusion of cross-modal edges ensures future predictions are meaningful in either modality.

We then rank all pairs according to this score and show the highest-scoring pairs of present and future frames detected in previously unseen videos (examples belo…

What Are Model Ensembles and Cascades?

Compared to a single model, ensembles can provide improved accuracy if there is variety in the collected models’ predictions.

To encourage the adoption of model ensembles, we demonstrate the following beneficial properties:Simple to build: Ensembles do not require complicated techniques (e.g., early exit policy learning).

On-device speedup: The reduction in computation cost (FLOPS) successfully translates to a speedup on real hardware.

In some cases it is not the average computation cost but the worst-case cost that is the limiting factor.

When solving a new ML problem, it’s common to start by experimenting with a subset of your data in a notebook environment. But if you want to execute a long-running job, add accelerators, or run multiple training trials with different input parameters, you’ll likely find yourself copying code over to a Python file to do the actual computation. That’s why we’re excited to announce the launch of the notebook executor, a new feature of Vertex AI Workbench that allows you to schedule notebooks ad hoc, or on a recurring basis. With the executor, your notebook is run cell by cell on Vertex AI Training. You can seamlessly scale your notebook workflows by configuring different hardware options, pas…

Today, we’re incredibly excited to announce the general availability of Vertex Pipelines.One of the best ways to scale your machine learning (ML) workflows is to run them as a pipeline, where each pipeline step is a distinct piece of your ML process. Pipelines are a great tool for productionizing, sharing, and reliably reproducing ML workflows across your organization. They are also the key to MLOps – with Pipelines you can build systems to automatically retrain and deploy models. In this post, we’ll show what you can do with Vertex Pipelines, and we’ll end by sharing a sample pipeline to help you get started.Vertex Pipelines in a nutshellLet’s briefly discuss what an ML pipeline does. A ma…

Earlier this year, we launched Contactless Sleep Sensing in Nest Hub, an opt-in feature that can help users better understand their sleep patterns and nighttime wellness.

Today we announced enhancements to Sleep Sensing that provide deeper sleep insights.

To help people understand their sleep patterns, Nest Hub displays a hypnogram, plotting the user’s sleep stages over the course of a sleep session.

Snoring sounds that are synchronized with the user’s breathing pattern (left) will be displayed in the user’s Nest Hub’s Snoring timeline.

Since Nest Hub with Sleep Sensing launched, researchers have expressed interest in investigational studies using Nest Hub’s digital quantification of nightt…

This week, the annual conference on Empirical Methods in Natural Language Processing (EMNLP 2021) will be held both virtually and in Punta Cana, Dominican Republic.

As a Diamond Level sponsor of EMNLP 2021, Google will contribute research on a diverse set of topics, including language interactions, causal inference, and question answering, additionally serving in various levels of organization in the conference.

Below is a full list of Google’s involvement and publications being presented at EMNLP 2021.

We congratulate these authors, and all other researchers who are presenting their work at the conference (Google affiliations presented in bold).

Organizing CommitteePublicationsMATE: Multi-…

Since the launch of our API, we’ve made deploying applications faster and more streamlined while adding new safety features.

Starting today, developers in supported countries can sign up and start experimenting with our API right away.

Tens of thousands of developers are already taking advantage of powerful AI models through our platform.

As another step in this direction, we are also updating our Content Guidelines to clarify what kind of content our API can be used to generate.

As our safeguards continue to improve, we will expand how the API can be used while further improving the experience for our users.

We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model.

However, they struggle to perform tasks that require accurate multistep reasoning, like solving grade school math word problems.

GSM8K DatasetGSM8K consists of 8.5K high quality grade school math word problems.

Training Verifiers: Models that Learn from their MistakesOne significant challenge in mathematical reasoning is the high sensitivity to individual mistakes.

Grade school math is an ideal testbed for these capabilities.

Our model works by first summarizing small sections of a book, then summarizing those summaries into a higher-level summary, and so on.

A zero-shot question-answering model can use our model’s summaries to obtain state-of-the-art on the NarrativeQA dataset for book-length question answering.

Our Approach: Combining Reinforcement Learning from Human Feedback and Recursive Task DecompositionConsider the task of summarizing a piece of text.

In the past we found that training a model with reinforcement learning from human feedback helped align model summaries with human preferences on short posts and articles.

In this case we break up summarizing a long piece of text into summarizing several sh…

Today, we’re excited to announce the appointment of Helen Toner to our Board of Directors.

As the Director of Strategy at Georgetown’s Center for Security and Emerging Technology (CSET), Helen has deep expertise in AI policy and global AI strategy research.

I greatly value Helen’s deep thinking around the long-term risks and effects of AI,” added Greg Brockman, OpenAI’s chairman and Chief Technology Officer.

“We are delighted to add her leadership to our board.”OpenAI is a unique organization in the AI research space, and has produced some of the advances, publications, and products I’m most excited about,” said Helen Toner.

She previously advised policymakers and grantmakers on AI strategy…

We are now inviting businesses and developers to build on top of OpenAI Codex through our API.

Watch Video Creating a Space Game with OpenAI Codex Tweet Watch Video “Hello World” with OpenAI Codex Tweet Watch Video Data Science with OpenAI Codex Tweet Watch Video Talking to Your Computer with OpenAI Codex Tweet Watch Video Converting Python to Ruby with OpenAI Codex Tweet Watch Video Giving OpenAI Codex a First Grade Math Test TweetOpenAI Codex is a descendant of GPT-3; its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories.

OpenAI Codex empowers computers to better understand people…

These issues can be mitigated by writing specialized GPU kernels, but doing so can be surprisingly difficult due to the many intricacies of GPU programming.

This has led us to extend and improve Triton, a recent language and compiler whose original creator now works at OpenAI.

# Z,X,Y are dense tensors Z[idx] = X[idx] + Y[idx] ... grid = (ceil_div(N, BLOCK),) block = (BLOCK,) add[grid, block](x, y, z, x.shape[0]) BLOCK = 512 # This is a GPU kernel in Triton.

Without a system like Triton, non-trivial modifications of matrix multiplication kernels would be out-of-reach for developers without exceptional GPU programming expertise.

If you’re interested in joining our team and working on Triton …

We've found we can improve language model behavior with respect to specific behavioral values by fine-tuning on a curated dataset of <100 examples of those values.

Appropriate or desirable language model behavior, like appropriate human behavior, cannot be reduced to one universal standard; desirable behavior differs by application and social context.

Step Two: Crafting the Dataset and Fine-TuningWe crafted a values-targeted dataset of 76 text samples; each sample was in a question-answer format and between 40 and 340 words.

But we believe this only scratches the surface and leaves important questions unanswered:Who should be consulted when designing a values-targeted dataset?

Please reach …

Investing in startups with big ideas about AI.

My advice to someone starting in deep learning research is to take your time to understand insights from fundamental papers and remember that the field is still relatively new.

Blogplaycircle Feedback Loops in Opinion ModelingDanielle Ensign OpenAI Mentor: Jeff WuPrevious Roles: Software Engineer at ITHAKA, Brighten AI, and Phylliida I have a background in Software Development, AI Fairness, and VR Game Development.

My project is exploratory, investigating prior work on opinion modeling from the context of deep learning.

Blogplaycircle Characterizing Test Time Compute on Graph Structured ProblemsKudzo Ahegbebu OpenAI Mentor: William GussPrevious Roles: Software Engineer at Facebook and Genen…

OpenAI is committed to developing general-purpose artificial intelligence that benefits all humanity, and we believe that achieving our goal requires expertise in public policy as well as technology.

So, we’re delighted to announce that Congressman Will Hurd has joined our board of directors.

Will served three terms in the U.S. House of Representatives, has been a leading voice on technology policy, and coauthored bipartisan legislation outlining a national strategy for artificial intelligence.

“Will brings a rare combination of expertise—he deeply understands both artificial intelligence as well as public policy, both of which are critical to a successful future for AI,” said Sam Altman, O…

Given any text prompt like a phrase or a sentence, GPT-3 returns a text completion in natural language.

Applications and industriesTo date, over 300 apps are using GPT-3 across varying categories and industries, from productivity and education to creativity and games.

Using GPT-3, Viable identifies themes, emotions, and sentiment from surveys, help desk tickets, live chat logs, reviews, and more.

Algolia Answers helps publishers and customer support help desks query in natural language and surface nontrivial answers.

With natural language processing, technical experience is no longer a barrier, and we can truly keep our focus on solving real world problems.

discovered that the human brain possesses multimodal neurons.

Now, we’re releasing our discovery of the presence of multimodal neurons in CLIP.

Our discovery of multimodal neurons in CLIP gives us a clue as to what may be a common mechanism of both synthetic and natural vision systems—abstraction.

Indeed, these neurons appear to be extreme examples of “multi-faceted neurons,” neurons that respond to multiple distinct cases, only at a higher level of abstraction.

How multimodal neurons composeThese multimodal neurons can give us insight into understanding how CLIP performs classification.

ZEISS partnered with Microsoft to power the ZEISS Medical Ecosystem with the Azure Healthcare APIs. Azure Healthcare APIs allow for data to be standardized and exchanged between the ZEISS medical centers and their devices across the world while leveraging Microsoft's geo-support for data residency requirements.

Tutel provides great compatibility with rich features to ensure the great performance when working on the Azure NDm A100 v4 cluster.

Unfortunately, existing machine learning frameworks have not provided an efficient all-to-all communication library, resulting in performance regression for large-scale distributed training.

Tutel achieves a 2.56x to 5.93x all-to-all speedup with 512 A100 GPUs for message sizes hundreds of MiB large.

Meta has been using Tutel to train its large language model, which has an attention-based neural architecture similar to GPT-3, on Azure NDm A100 v4.

Table 1 summarizes the detailed parameter setting of the model, and Figure 3 shows the 40 percent speedup Tutel ac…

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

Today, we’re excited to announce the release of SynapseML (previously MMLSpark), an open-source library that simplifies the creation of massively scalable machine learning (ML) pipelines.

Building production-ready distributed ML pipelines can be difficult, even for the most seasoned developer.

A unified API standardizes many of today’s tools, frameworks, and algorithms, streamlining the distributed ML experience.

Figure 1: The simple API abstracts over many different ML frameworks, scales, computation paradigms, data providers, and languages.

It even includes templates to quickly prototype distributed ML systems, such as visual search engines, predictive maintenance pipelines, document tran…

This blog post discusses emerging research on combining techniques to ensure privacy and confidentiality when using sensitive data to train ML models.

Mitigate: We then develop and apply mitigation strategies, such as differential privacy (DP), described in more detail later in this blog post.

When training or fine-tuning machine learning models on customer content, we adhere to strict policy regarding the privacy budget[1].

This enables the collaboration of multiple customers on private data without the need to trust the cloud provider.

Finally, can we tightly integrate privacy and confidentiality guarantees into the design of the next generation of deep learning models?

Azure’s market-leading vision for HPC and AI is based on a commitment to continual improvement and backed by a core of genuine and recognized HPC expertise, using proven HPC technology and design principles, enhanced with the best features of the cloud. The result is a capability that delivers performance, scale, and value unlike any other cloud.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

Today, the Microsoft Turing team is thrilled to introduce Turing Bletchley, a 2.5-billion parameter Universal Image Language Representation model (T-UILR) that can perform image-language tasks in 94 languages.

This model shows uniquely powerful capabilities and a groundbreaking advancement in image language understanding.

Today’s image retrieval systems depend heavily on text metadata that is available for images, e.g., image captions, alt-text, surrounding text, image URL, etc.

Moreover, the demo was built directly with the pre-trained model and not finetuned with any image retrieval task.

We expect the T-Bletchley model to improve image question and answering, image search, and image-to-i…

The natural association between visual observations and their corresponding sounds has exhibited powerful self-supervision signals for learning video representations, which makes the ever-growing amount of online video an attractive data source for self-supervised learning.

This severely limits the utility of online videos for self-supervised learning, which begs the question: How can we fully leverage online videos without extensive human effort?

Today, in collaboration with Seoul National University and NVIDIA Research, we are thrilled to release an automatic dataset curation pipeline and the largest video dataset for self-supervised audio-visual learning, dubbed ACAV100M (/ˈärˌkīv/; auto…

This year during NVIDIA GTC 21, we’re spotlighting some of the most transformational applications powered by NVIDIA accelerated computing that highlights our commitment to edge, on-premises, and cloud computing. Registration is free, so sign up to learn how Microsoft is powering transformation.

If this message persists and you would like to contact Azure Support, please connect with @AzureSupport on TwitterRef A: DDF9700AE37D4BAC98175A759D542F30 Ref B: FRAEDGE0716 Ref C: 2021-11-04T19:22:21Z

From research to diagnosis to treatment, AI has the potential to improve outcomes for some treatments by 30 to 40 percent and reduce costs by up to 50 percent. Obtaining the large data sets necessary for generalizability, transparency, and reducing bias has historically been difficult and time-consuming. That’s why the University of California, San Francisco (UCSF) collaborated with Microsoft, Fortanix, and Intel to create BeeKeeperAI.

This website is using a security service to protect itself from online attacks.

The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

Inspired by teachable object recognizersThe ORBIT dataset and benchmark are inspired by a real-world application for the blind and low-vision community: teachable object recognizers.

Unlike typical computer vision benchmarks, performance on the teachable object recognition benchmark is measured based on input from each user.

Evaluations on highly cited few-shot learning models show that there is significant scope for innovation in high-variation, few-shot learning.

Despite saturation of model performance on existing few-shot benchmarks, few-shot models only achieve 50-55% accuracy on the teachable object recognition benchmark.

Research to realize human-AI collaborationCreating teachable obj…

Inspired by teachable object recognizersThe ORBIT dataset and benchmark are inspired by a real-world application for the blind and low-vision community: teachable object recognizers.

Unlike typical computer vision benchmarks, performance on the teachable object recognition benchmark is measured based on input from each user.

Evaluations on highly cited few-shot learning models show that there is significant scope for innovation in high-variation, few-shot learning.

Despite saturation of model performance on existing few-shot benchmarks, few-shot models only achieve 50-55% accuracy on the teachable object recognition benchmark.

Research to realize human-AI collaborationCreating teachable obj…

“It’s surreal, there’s no playbook for running a global company in a pandemic over Zoom.

“The goal is to find ways to unlock more of our brainpower through a multidisciplinary approach between computing and management,” says Houston.

I think academia has a huge role to play [here], and I think MIT is super well-positioned to lead.

Dropbox proceeded to redesign the work experience throughout the company, unveiling a “virtual first” working model in October 2020 in which remote work is the primary experience for all employees.

It’s a transition that’s been underway for a while, but Covid completely finished the swing,” says Houston.

Using this massive dataset, they tested the limits of what machine learning could do.

However, using GAN models alone would result in homogeneous designs that lack novelty and can’t be assessed in terms of performance.

Using this approach, Ahmed’s team developed an open-source computational design tool for bicycles freely available on their lab website.

He hopes the computational design tools he develops could lead to “design democratization,” putting more power in the hands of the end user.

While Deng is utilizing simulations and machine learning at the molecular level to improve designs, others are taking a more macro approach.

Nine technologies developed at MIT Lincoln Laboratory have been selected as R&D 100 Award winners for 2021.

Global Synthetic Weather RadarThe Global Synthetic Weather Radar (GSWR) provides radar-like weather imagery and radar-forward forecasts for regions where actual weather radars are not deployed or are limited in range.

Since 2010, Lincoln Laboratory has received 75 R&D 100 Awards.

"Our R&D 100 Awards recognize the significant, ongoing technology development and transition success at the laboratory.

Editors of R&D World announced the 2021 R&D 100 Award winners at virtual ceremonies broadcast on October 19, 20, and 21.

The members of her lab study fuel cells, which convert hydrogen and oxygen into electricity (and water).

What brings all this together in her lab is the electrochemistry of ionic-electronic oxides and their interfaces.

Yildiz stayed at MIT for a postdoctoral fellowship, between the nuclear engineering and mechanical engineering departments, studying electrochemistry in fuel cells.

Substitutions — for instance, strontium atoms — added to the crystal enhance its ability to transport electrons and oxygen ions.

In retrospect, Yildiz says, the leap from her formal training on nuclear engineering into electrochemistry and materials was a big one.

This ability to manipulate anything from a cup to a tuna can to a Cheez-It box could help the hand quickly pick-and-place objects in specific ways and locations — and even generalize to unseen objects.

DeepMind created “ RGB-Stacking ,” a vision-based system that challenges a robot to learn how to grab items and stack them.

Not only does the robot need to manipulate the object, but also circumvent gravity so it doesn’t fall down.

Chen wrote a paper about the research alongside MIT CSAIL PhD student Jie Xu and MIT Professor Pulkit Agrawal.

The research is funded by Toyota Research Institute, Amazon Research Award, and DARPA Machine Common Sense Program.

Their technique involves removing unnecessary parts of a common, but complex, speech recognition model and then making minor adjustments so it can recognize a specific language.

Learning speech from audioThe researchers studied a powerful neural network that has been pretrained to learn basic speech from raw audio, called Wave2vec 2.0.

This opens the door for speech recognition of uncommon languages that lack large amounts of transcribed speech, like Wolof, which is spoken by 5 million people in West Africa.

Lai and his collaborators wanted to see how the pruning process would affect this model’s speech recognition performance.

In the first step, a pretrained speech recognition neural netwo…

The core goals of the Common Ground are to infuse computing education throughout MIT in a coordinated manner, as well as to serve as a platform for multi-departmental collaborations.

As much as computing is changing thinking in the disciplines, the disciplines are changing the way people develop new computing approaches.

Q: How is the Common Ground facilitating collaborations and engaging faculty across MIT to develop new curricula?

Grossman: The Common Ground Standing Committee was formed to oversee the activities of the Common Ground and is charged with evaluating how best to support and advance program objectives.

It is very exciting to see MIT students having access to these unique offe…

By enriching AI algorithms with new processors, A3D3 seeks to speed up AI algorithms for solving fundamental problems in collider physics, neutrino physics, astronomy, gravitational-wave physics, computer science, and neuroscience.

“We realized all the AI algorithms were designed to solve much slower problems, such as image and voice recognition.

FPGAs and other new processor types, such as graphics processing units (GPUs), accelerate AI algorithms to more quickly analyze huge amounts of data.

The Huge Data from the Large Hadron ColliderWith data rates already exceeding 500 terabits per second, the LHC processes more data than any other scientific instrument on earth.

Its future aggregate d…

One of the long-standing puzzles in neutrino physics comes from the Mini Booster Neutrino Experiment (MiniBooNE), which ran from 2002 to 2017 at the Fermi National Accelerator Laboratory, or Fermilab, in Illinois.

Due to the effects of neutrino oscillations, this sterile neutrino would manifest itself as an enhancement of electron neutrinos in MiniBooNE.

There are many additional anomalies seen in neutrino physics that indicate this particle might exist.

Why use deep learning in neutrino physics?

Our group produced the first paper on particle identification using deep learning in neutrino physics, proving it to be a powerful technique.

With so many cases, some agencies are implementing machine learning models to help child welfare specialists screen cases and determine which to recommend for further investigation.

Researchers at MIT and elsewhere launched a research project to identify and tackle machine learning usability challenges in child welfare screening.

In collaboration with a child welfare department in Colorado, the researchers studied how call screeners assess cases, with and without the help of machine learning predictions.

But a big takeaway from this project is that these domain experts don’t necessarily want to learn what machine learning actually does.

Keeping the screeners in the loop throughout the itera…

“Roboat” has come a long way since the team first started prototyping small vessels in the MIT pool in late 2015.

Last year, the team released their half-scale, medium model that was 2 meters long and demonstrated promising navigational prowess.

If you don’t get seasick, an autonomous boat might be the right mode of transportation for you.

Autonomous Roboats set sea in the Amsterdam canals and can comfortably carry up to five people, collect waste, deliver goods, and provide on-demand infrastructure.

“Roboat’s control system is adaptive to the number of people in the boat.”To swiftly navigate the bustling waters of Amsterdam, Roboat needs a meticulous fusion of proper navigation, perception…

The most recent generation of predictive language models also appears to learn something about the underlying meaning of language.

In the past few years, artificial intelligence models of language have become very good at certain tasks.

Making predictionsThe new, high-performing next-word prediction models belong to a class of models called deep neural networks.

They found that the best-performing next-word prediction models had activity patterns that very closely resembled those seen in the human brain.

“One of the challenges of language processing is the real-time aspect of it,” he says.

They then use a machine learning system known as a neural network to convert the 2D image into a representation of the microbiome present in the 3D environment.

To figure out how to communicate these data back to the research team, Xia drew upon her master’s degree research.

The communication devices can be used to relay data about the ocean environment from the machine learning algorithms.

By combining these low-cost communication devices along with microscopic images and machine learning, Xia hopes to design a low-cost, real-time monitoring system that can be scaled to cover entire seaweed farms.

While Xia still daydreams about opening a restaurant, she hopes the seaweed project will prom…

The fourth annual Massachusetts STEM Week kicked off on Monday, Oct. 18 at the MIT Media Lab.

Organized by the Massachusetts Executive Office of Education and the STEM Advisory Council, Mass STEM Week is a statewide effort to boost awareness, interest, and access in STEM education and career opportunities for learners of all ages and backgrounds.

For the first year, MIT RAISE has created age-appropriate curriculum modules for grades 3-5, 6-8, and 9-12, including those with little or no technology experience.

Jeffrey Leiden, executive chair of Vertex Pharmaceuticals and a supporter of Mass STEM Week, also attended the opening event; Vertex Pharmaceuticals is a founding sponsor of Day of AI.

…

Sequence Modeling Solutionsfor Reinforcement Learning ProblemsSequence Modeling Solutions for Reinforcement Learning ProblemsLong-horizon predictions of (top) the Trajectory Transformer compared to those of (bottom) a single-step dynamics model.

While it has been possible to apply reinforcement learning algorithms to large-scale problems, generally there has been much more friction in doing so.

In this post, we explore whether we can alleviate these difficulties by tackling the reinforcement learning problem with the toolbox of sequence modeling.

Though there is value in studying the most streamlined approaches that can tackle reinforcement learning problems, it is possible that the most ef…

Which Mutual Information Representation Learning Objectives are Sufficient for Control?

To simplify the analysis, we analyze representation learning in isolation from the other aspects of RL by assuming the existence of an offline dataset on which to perform representation learning.

An objective may have more than one maximizing representation, so we call a representation learning objective sufficient if all the representations that maximize that objective are sufficient.

To separate representation learning from RL, we first optimize each representation learning objective on a dataset of offline data, (similar to the protocol in Stooke et al.

This post is based on the paper Which Mutual Inf…

Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain DatasetsFig.

For comparison, we include the performance of the policy when trained only on the target domain data, without bridge data (Target Domain Only), a baseline that uses only the bridge data without any target domain data (Direct Transfer), as well as a baseline that trains a single-task policy on data in the target domain only (Single Task).

However, we do include the “direct transfer” baseline, which utilizes a policy trained only on the bridge data.

Figure 10: Example rollouts of policies jointly trained on target domain data and bridge data in each of the three transfer scenarios.

This means that bridge…

Not FoundThe requested URL /blog/2021/11/18/mi-sufficiency-analysis/ was not found on this server.

Which Mutual Information Representation Learning Objectives are Sufficient for Control?

To simplify the analysis, we analyze representation learning in isolation from the other aspects of RL by assuming the existence of an offline dataset on which to perform representation learning.

An objective may have more than one maximizing representation, so we call a representation learning objective sufficient if all the representations that maximize that objective are sufficient.

To separate representation learning from RL, we first optimize each representation learning objective on a dataset of offline data, (similar to the protocol in Stooke et al.

This post is based on the paper Which Mutual Inf…

How should we compare neural network representations?

To understand neural networks, researchers often use similarity metrics to measure how similar or different two neural networks are to each other.

Thus, for a given pair of neural network representations, we measure both their (dis)similarity and the difference between their accuracies on some task.

“Insights on representational similarity in neural networks with canonical correlation.” Proceedings of the 32nd International Conference on Neural Information Processing Systems.

Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2020.

Epistemic POMDPs and Implicit Partial ObservabilityActively steering towards regions of low uncertainty or taking information-gathering actions are two of a multitude of avenues an RL agent has to handle its epistemic uncertainty.

What makes the epistemic POMDP particularly exciting is the following equivalence:An RL agent is Bayes-optimal for generalization if and only if it maximizes expected return in the corresponding epistemic POMDP.

LEEP, an algorithm based on the epistemic POMDP objective, generalizes better than PPO in four Procgen tasks.

This is surprisingly not true; limited training data in RL introduces implicit partial observability into an otherwise fully-observable problem.

T…

RECON: Learning to Explore the Real World with a Ground RobotAn example of our method deployed on a Clearpath Jackal ground robot (left) exploring a suburban environment to find a visual target (inset).

To build a robot capable of exploring and navigating like this, we need to learn from diverse prior datasets in the real world.

Our method learns both components using datasets or real-world robot interactions gathered in prior work.

In a new environment, RECON encourages the robot to explore at the frontier of the map – while the robot is not at the frontier, RECON directs it to navigate to a previously seen subgoal at the frontier of the map.

This dataset presents an exciting benchmark f…

RECON: Learning to Explore the Real World with a Ground RobotAn example of our method deployed on a Clearpath Jackal ground robot (left) exploring a suburban environment to find a visual target (inset).

To build a robot capable of exploring and navigating like this, we need to learn from diverse prior datasets in the real world.

Our method learns both components using datasets or real-world robot interactions gathered in prior work.

In a new environment, RECON encourages the robot to explore at the frontier of the map – while the robot is not at the frontier, RECON directs it to navigate to a previously seen subgoal at the frontier of the map.

This dataset presents an exciting benchmark f…

Epistemic POMDPs and Implicit Partial ObservabilityActively steering towards regions of low uncertainty or taking information-gathering actions are two of a multitude of avenues an RL agent has to handle its epistemic uncertainty.

What makes the epistemic POMDP particularly exciting is the following equivalence:An RL agent is Bayes-optimal for generalization if and only if it maximizes expected return in the corresponding epistemic POMDP.

LEEP, an algorithm based on the epistemic POMDP objective, generalizes better than PPO in four Procgen tasks.

This is surprisingly not true; limited training data in RL introduces implicit partial observability into an otherwise fully-observable problem.

T…

Designs from Data: Offline Black-Box Optimization via Conservative TrainingFigure 1: Offline Model-Based Optimization (MBO): The goal of offline MBO is to optimize an unknown objective function $f(x)$ with respect to $x$, provided access to only as static, previously-collected dataset of designs.

Conventionally, such problems have been tackled with black-box optimization procedures that repeatedly query an objective function.

We call this offline model-based optimization (offline MBO), and in this post, we discuss offline MBO methods and some recent advances.

Offline Model-Based Optimization (Offline MBO)Formally, the goal in offline model-based optimization is to maximize a black-box objec…

Designs from Data: Offline Black-Box Optimization via Conservative TrainingFigure 1: Offline Model-Based Optimization (MBO): The goal of offline MBO is to optimize an unknown objective function $f(x)$ with respect to $x$, provided access to only as static, previously-collected dataset of designs.

Conventionally, such problems have been tackled with black-box optimization procedures that repeatedly query an objective function.

We call this offline model-based optimization (offline MBO), and in this post, we discuss offline MBO methods and some recent advances.

Offline Model-Based Optimization (Offline MBO)Formally, the goal in offline model-based optimization is to maximize a black-box objec…

Measures of generalization performance for neural networks trained on four different boolean functions (colors) with varying training set size.

It turns out this is possible: in our recent paper, we derive a first-principles theory that allows one to make accurate predictions of neural network generalization (at least in certain settings).

Finite network $\approx$ infinite-width network $=$ kernel regressionA major vein of deep learning theory in the last few years has studied neural networks of infinite width.

Trusting that this first approximation will bear weight, our challenge now is to understand kernel regression.

It also applies to linear regression, a special case of kernel regressi…

Measures of generalization performance for neural networks trained on four different boolean functions (colors) with varying training set size.

It turns out this is possible: in our recent paper, we derive a first-principles theory that allows one to make accurate predictions of neural network generalization (at least in certain settings).

Finite network $\approx$ infinite-width network $=$ kernel regressionA major vein of deep learning theory in the last few years has studied neural networks of infinite width.

Trusting that this first approximation will bear weight, our challenge now is to understand kernel regression.

It also applies to linear regression, a special case of kernel regressi…

Making RL Tractable by Learning More Informative Reward Functions: Example-Based Control, Meta-Learning, and Normalized Maximum LikelihoodDiagram of MURAL, our method for learning uncertainty-aware rewards for RL.

Conditional Normalized Maximum Likelihood (CNML)One method particularly well-suited for this task is Conditional Normalized Maximum Likelihood (CNML).

Meta-learning techniques are particularly well suited to our class of computational problems since it involves quickly solving multiple different maximum likelihood problems to evaluate the CNML likelihood.

In doing so, meta-learning provides us an effective tool for producing estimates of normalized maximum likelihood significantly…

PrerequisitesFor this walkthrough, you should have the following prerequisites:A GitLab accountAn AWS accountAn Amazon SageMaker Studio domain (with SageMaker projects enabled)This post provides a detailed explanation of the SageMaker projects, GitLab, and GitLab pipelines integration.

The model build pipeline automatically triggers and runs the pipeline from end to end whenever a new commit is made to the model build repository.

Model Build Repository Name – The name of the repository mlops-gitlab-project-seedcode-model-build of the model build and training seed code.

Model build pipeline in GitLabLet’s review the GitLab pipelines, starting with the model build pipeline.

Model deployment p…

This post goes deeper into our deep learning natural language processing (NLP) based advertisement predictor, how we integrated the predictor into one of our pipelines using Deep Java Library (DJL), and how that change made our architecture simpler and MLOps easier.

DJL is an open source Java framework for deep learning created by AWS.

Today’s practices are that deploying deep learning models incurs complexity by itself.

ONNXONNX is an open-source ecosystem of AI and ML tools designed to provide extensive interoperability between different deep learning frameworks.

This maintains our confidence that our deep learning model works properly whenever we change our code base.

In this post, we share how Careem detects identity fraud using graph-based deep learning and Amazon Neptune.

After much experimentation, Careem decided to focus on the identity of users, and came up with a powerful way to outsmart any efforts of identity fraud.

Architecture overviewBefore we dive deep into how Careem used Neptune an identity graph for fraud detection, let’s look at the current architecture underpinning the solution.

Historical data – Careem uses Apache Hive running on Amazon Simple Storage Service (Amazon S3) to extract data and push it to Amazon EMR with PySpark.

In this case, we decided to build a graph model to predict the is_fraud property of customer nodes using Amazon…

Additionally, Amazon Redshift ML allows data scientists to either import existing SageMaker models into Amazon Redshift for in-database inference or remotely invoke a SageMaker endpoint.

PrerequisitesTo get started, we need an Amazon Redshift cluster with the Amazon Redshift ML feature enabled.

For an introduction to Amazon Redshift ML and instructions on setting it up, see Create, train, and deploy machine learning models in Amazon Redshift using SQL with Amazon Redshift ML.

Get the SageMaker model endpointOn the Amazon SageMaker console, under Inference in the navigation pane, choose Endpoints to find your model name.

For more information about building different models with Amazon Redshi…

Today we announce the general availability of Syne Tune, an open-source Python library for large-scale distributed hyperparameter and neural architecture optimization.

With Syne Tune, you can run hyperparameter and neural architecture tuning jobs locally on your machine or remotely on Amazon SageMaker by changing just one line of code.

By open-sourcing Syne Tune, we hope to create a community that brings together academic and industrial researchers in machine learning (ML).

Tune hyperparameters with Syne TuneWe now detail how to tune hyperparameters with Syne Tune.

Run large-scale tuning jobs with Syne Tune and SageMakerThe previous example showed how to tune hyperparameters on a local mach…

Breakout sessionsPrepare data for ML with ease, speed, and accuracy (AIM319)Join this session to learn how to prepare data for ML in minutes using Amazon SageMaker.

Then, hear from Vanguard as they share their journey enabling MLOps to achieve ML at scale for their polyglot model development platforms using SageMaker features, including SageMaker projects, SageMaker Pipelines, SageMaker Model Registry, and SageMaker Model Monitor.

Forecast automatically generates highly accurate forecasts using ML, explains the drivers behind those forecasts, and keeps your ML models always up to date to capture new trends.

Workshop sessionsDevelop your ML project with Amazon SageMaker (AIM402)In this works…

The AWS AI/ML Community has compiled a series of session guides to AWS re:Invent 2021 to help you get the most out of re:Invent this year.

AWS re:Invent 2021: How to maximize your in-person learning experience as a new Machine Learning practitioner, from AWS ML Community Builder Martin Paradesi.

From our new Egypt-based AWS ML Hero Salah Elhossiny: Top 5 AWS ML Sessions to Attend at AWS re:Invent 2021.

Top 5 Sessions for AI/ML Developers at AWS re:Invent 2021, from AWS ML Community Builder Brooke Jamieson.

About the AuthorPaxton Hall is a Marketing Program Manager for the AWS AI/ML Community on the AI/ML Education team at AWS.

We’re excited to launch explainability impact scores in Amazon Forecast, which help you understand the factors that impact your forecasts for specific items and time durations of interest.

Impact scores measure the relative impact attributes have on forecast values.

Generate explainability impact scoresIn this section, we walk through how to generate explainability impact scores for your forecasts using the Forecast console.

To export all the impact scores, choose Create explainability export in the Explainability exports Provide the export details and choose Create explainability export.

The explainability export file provides two type of impact scores: normalized impact scores and raw imp…

However, in doing so, you have to train your entire forecasting model again, which is a time-consuming process.

This launch is accompanied with new pricing, which you can review at Amazon Forecast pricing.

Jitendra Bangani is an Engineering Manager at AWS, leading a growing team of curious and driven engineers for Amazon Forecast.

Hilaf Hasson is a Machine Learning Scientist at AWS, and currently leads the R&D team of scientists working on Amazon Forecast.

Adarsh Singh works as a Software Development Engineer in the Amazon Forecast team.

To help fans understand a coach’s decision, the NFL and AWS partnered to create the Next Gen Stats Decision Guide.

The Next Gen Stats Decision Guide is a suite of machine learning (ML) models designed to determine the optimal fourth-down call.

By comparing the odds of winning the game for each fourth-down choice, the Next Gen Stats Decision Guide provides a data-driven answer to that optimal fourth-down call.

Based on these expected probabilities, the Next Gen Stats Decision Guide recommends going for it with a 13% difference.

The odds of converting on a fourth-down play are a key component of the Next Gen Stats Decision Guide.

Amazon SageMaker Ground Truth supports many different types of labeling jobs, including several image-based labeling workflows like image-level labels, bounding box-specific labels, or pixel-level labeling.

Ground Truth jobsEach custom Ground Truth labeling job is defined with a web-based user interface and two associated AWS Lambda functions (for more information, see Processing with AWS Lambda).

For custom Ground Truth user interfaces, a set of custom tags is available, known as Crowd tags.

To learn more about chained Ground Truth jobs, see Chaining Labeling Jobs.

For more information about the Crowd tags you can use in the Ground Truth UI, see Crowd HTML Elements Reference.

Design your Data Wrangler flow fileTo get started – on the Studio File menu, choose New, and choose Data Wrangler Flow.

This launches a Data Wrangler instance and configures it with the Data Wrangler app.

Load the data from Amazon S3 into Data WranglerTo load the data into Data Wrangler, complete the following steps:On the Import tab, choose Amazon S3 as the data source.

With the built-in bias report in Data Wrangler, data scientists can quickly detect bias during the data preparation stage of the ML workflow.

This no-code/low-code capability of Data Wrangler accelerates training data preparation and increases data scientist agility with faster iterative data preparation.

AWS recently released the new Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK).

The new SageMaker ACK Operators make it easier for machine learning (ML) developers and data scientists who use Kubernetes as their control plane to train, tune, and deploy ML models in Amazon SageMaker without signing in to the SageMaker console.

ACK includes a common controller runtime, a code generator, and a set of AWS service-specific controllers, one of which is the SageMaker controller.

Alice issues a call to kubectl apply , passing in a file that describes a Kubernetes custom resource describing her SageMaker training job.

The SageMaker controller then communicates…

Plus, kick back for the holiday with four new games coming to the GeForce NOW library this week.

With GeForce NOW, nearly any device can become a GeForce gaming rig.

GeForce NOW Priority Membership digital gift cards are now available in 2-month, 6-month or 12-month options.

Existing Founders and Priority members will have the number of months added to their accounts.

We initially planned to add Farming Simulator 2022 to GeForce NOW in November, but discovered an issue during our onboarding process.

See the latest innovations spanning from the cloud to the edge at AWS re:Invent.

Product Marketing Manager of NVIDIA, discusses model deployment challenges, how NVIDIA Triton simplifies deployment and maximizes performance of AI models, how to use NVIDIA Triton on AWS, and a customer use case.

However, the process of training, optimizing, and running ML models to build AI-powered applications is complex and requires expertise.

The NVIDIA NGC catalog provides GPU-optimized AI software including frameworks, pretrained models, and industry-specific software development keys (SDKs) that accelerate workflows.

This software allows data engineers, data scientists, developers, and DevOps teams to f…

Data scientists and machine learning engineers use many methods, techniques, and tools to prep, build, train, deploy, and optimize their machine learning models.

NVIDIA Merlin is designed to streamline recommender workflows.

Merlin Transformers4Rec, designed for recommenders and solving cold-start problemsRecommender methods popularized in mainstream media often rely upon long-term user profiles or lifetime user behavior.

The NVIDIA Merlin team designed Transformers4Rec to be used as a standalone solution or within an ensemble of recommendation models.

Download and try NVIDIA MerlinThe latest update to NVIDIA Merlin, including Transformers4Rec and SOK, strengthens streamlining and accelerat…

A picture worth a thousand words now takes just three or four words to create, thanks to GauGAN2, the latest version of NVIDIA Research’s wildly popular AI painting demo.

The new GauGAN2 text-to-image feature can now be experienced on NVIDIA AI Demos, where visitors to the site can experience AI through the latest demos from NVIDIA Research.

With the versatility of text prompts and sketches, GauGAN2 lets users create and customize scenes more quickly and with finer control.

The AI model behind GauGAN2 was trained on 10 million high-quality landscape images using the NVIDIA Selene supercomputer, an NVIDIA DGX SuperPOD system that’s among the world’s 10 most powerful supercomputers.

One examp…

Electric automaker Xpeng took the wraps off the G9 SUV this week at the international Auto Guangzhou show in China.

The intelligent, software-defined vehicle is built on the high-performance compute of NVIDIA DRIVE Orin and delivers AI capabilities that are continuously upgraded with each over-the-air update.

The Xpeng G9 and its fellow EVs are elevating the driving experience with intelligent features that are always at the cutting edge.

Xpilot 4.0 is built on two NVIDIA DRIVE Orin systems-on-a-chip (SoC), achieving 508 trillion operations per second (TOPS).

Charging AheadThe G9 is designed for the international market, bringing software-defined innovation to roads around the world.

This post shows how to integrate NVIDIA Operators into new edge AI platforms using preinstalled drivers.

There are two operators: the NVIDIA GPU Operator and the NVIDIA Network Operator.

These components make up the NVIDIA GPU OperatorThe NVIDIA Network Operator automates CONNECTX SmartNIC configuration for Kubernetes pods that need fast networking.

The NVIDIA Network Operator includes the following components:The NVIDIA OFED Driver container automates network driver and library installation.

It is compiled automatically during Linux driver installation if both the ib_core and NVIDIA GPU driver sources are present on the system.

More recently, the company was approached by ExxonMobil to help with a potentially deadly issue: improving worker safety around open oil tanks.

While this use case is highly specific, the company’s AI architecture is designed to flexibly support many different algorithms and functions.

To shorten training times further, Two-i is looking to test its huge image dataset on the powerful NVIDIA A100 GPU.

Two-i has also started benchmarking NVIDIA’s pretrained models against its algorithms and begun using the NVIDIA DeepStream SDK to enhance its video analytics pipeline.

Trombini recognizes that Two-i has to keep its focus on delivering one benefit after another to achieve the company’s long-term…

They came to network, get an update on the SIA’s work in Washington D.C., and bestow the 2021 Robert N. Noyce award, their highest honor, on the founder and CEO of NVIDIA.

Recognizing “an Icon”Turning to the Noyce award, Neuffer introduced Huang as “an icon in our industry.

“I accept this on behalf of all NVIDIA’s employees because it reflects their body of work,” Huang said.

In it, Andrew Ng, a machine-learning pioneer and entrepreneur described the pivotal role NVIDIA’s CEO has played in AI.

“His impact on the semiconductor industry, AI and the world is almost incalculable.”

With NVIDIA RTX graphics and NVIDIA CloudXR, Rendermedia helps businesses get their products in the hands of customers and audiences, allowing them to interact and engage collaboratively on any device, from any location.

With NVIDIA CloudXR, Rendermedia and its product manufacturing clients can quickly render and create fully interactive simulated products in photographic detail, while also reducing their time to market.

Rendermedia customers Airbus and National Grid are using VR experiences to showcase future products and designs in realistic scenarios.

And they can easily make changes to designs in real time, helping users iterate more often and get to final product designs quicker.

Learn…

With any new, paid six-month Priority or GeForce NOW RTX 3080 subscription, members will receive Crysis Remastered for free for a limited time.

GeForce NOW Can Run Crysis … And So Can YouBut can it run Crysis?

For a limited time, get a copy of Crysis Remastered free with select GeForce NOW memberships.

Purchase a six-month Priority membership, or the new GeForce NOW RTX 3080 membership, and get a free redeemable code for Crysis Remastered on the Epic Games Store.

Founders, exclusively, can upgrade to a GeForce NOW RTX 3080 membership and receive 10 percent off the subscription price and no risk to their current Founders benefits.

Fewer than 4,000 tigers remain worldwide, according to Tigers United, a university consortium that recently began using AI to help save the species.

Jeremy Dertien is a conservation biologist with Tigers United and a Ph.D. candidate in wildlife biology and conservation planning at Clemson University.

He spoke with NVIDIA AI Podcast host Noah Kravitz about a project deploying AI-equipped cameras to monitor poaching in central India, where more than 70 percent of the remaining tiger populations reside.

Now, scientists can take a closer look at endangered species by studying AI-generated 3D representations of them.

Subscribe to the AI PodcastGet the AI Podcast through iTunes, Google Podcasts, …

Get hands-on training in AI, deep learning, accelerated computing, and data science with the NVIDIA Deep Learning Institute (DLI).

DLI offers self-paced, online courses as well as instructor-led online workshops.

Thursday, Nov. 18, 7:00 a.m. – 3:00 p.m. PSTUse code DLISC21 to receive half-off full-price registration.

Monday, Dec. 6, 9:00 a.m. – 5:00 p.m. CETUse code DLIGTC50 to receive half-off full-price registration.

Tuesday, Dec. 14, 9:00 a.m. – 5:00 p.m. CETUse code DLIGTC50 to receive half-off full-price registration.

Editor’s Note: If you’re interested sharing your data science and AI expertise, you can apply to write for our blog here.

Data Scientists are intermediaries between the world of numerical representations of data and project stakeholders, therefore the ability to interpret and convey derived understanding from datasets is essential to Data Scientists.

Writing is one means of communication that equips Data scientists with the capability to convey and present ideas, patterns, and learning from data.

Data Scientists are ushering in the future one model at a time and it’s our responsibility to ensure that.

Through writing Data Scientists have the opportunity to enforce best practices in software…

But there’s a crucial skill that should be attained by Data Scientists, irrespective of their experience, and that is writing.

Machine Learning and Data science contents written On Twitter, LinkedIn, Facebook, Quora, and StackOverflow, all fall into this category.

Blogs and ArticlesMany experts believe that blogs and articles have a unique role in the machine learning community.

Books for Data Scientist and Machine Learning practitioners:You will find that most authors listed preceding have produced the majority if not all forms of writing listed in this article, regardless of their domain specialty, hence why I consider writing a vital skill for Machine Learning practitioners and Data Scie…

NVIDIA-powered systems won four of five tests in MLPerf HPC 1.0, an industry benchmark for AI performance on scientific applications in high performance computing.

They’re the latest results from MLPerf, a set of industry benchmarks for deep learning first released in May 2018.

What the Benchmarks MeasureMLPerf HPC 1.0 measured training of AI models in three typical workloads for HPC centers.

Compared to the best results in strong scaling from last year’s MLPerf 0.7 round, NVIDIA delivered 5x better results for CosmoFlow.

The MLPerf benchmarks are backed by MLCommons, an industry group led by Alibaba, Google, Intel, Meta, NVIDIA and others.

It shards an AI model’s parameters across data parallel workers and can optionally offload part of the training computation to the CPUs.

For example, typical data parallel training requires maintaining redundant copies of the model on each GPU, and model parallel training introduces additional communication costs to move activations between workers (GPUs).

Using FSDP in computer vision modelsFor computer vision models, FSDP is supported in VISSL and tested on RegNets architectures.

Users may need to carefully tune the activation checkpointing strategy to fit a large model within limited GPU memory space.

We look forward to developing algorithms for auto-tuning both GPU memory usage and trai…

We will be hosting a talk about our work on, “A Platform Agnostic Observability System for AI Accelerators” during our virtual Systems @Scale event at 10:20 a.m. PT on Wednesday, June 30, followed by a live Q&A session.

To meet these challenges, we’ve introduced three new tools:ASIC Monitoring (Asicmon) , a scalable observability framework.

However, with an accelerator system, we can imagine the CPU now has a complicated and brawnier sibling!

Since implementing Asicmon we’ve been able to increase our AI accelerator metrics support from ~30 percent to ~75 percentAtrace: Accelerator tracing at scaleWhy tracing?

This would allow us to debug the end-to-end latency of microservices that use AI a…

People upload hundreds of millions of videos to Facebook every day.

From a pure computing perspective, applying the most advanced codecs to every video uploaded to Facebook would be prohibitively inefficient.

A relatively small percentage (roughly one-third) of all videos on Facebook generate the majority of overall watch time.

The impact of the new video encoding modelIn addition to improving viewer experience with newly uploaded videos, the new model can identify older videos on Facebook that should have been encoded with more advanced encodings and route more computing resources to them.

The improved compression has also allowed people on Facebook with limited data plans, such as those i…

In this blog, we discuss how moving to distributed XGBoost on Ray helps address these concerns and how finding the right abstractions allows us to seamlessly incorporate Ray and XGBoost Ray into Uber’s ML ecosystem.

To run XGBoost Ray, we simply pass in XGBoost Ray’s train function to the Ray Estimator.

In this example, the XGBoost Ray Estimator will create 50 GPU workers that form the Ray Cluster, each participating in data-parallel XGBoost training and synchronization.

To go from running a single distributed XGBoost Ray job to running distributed hyperparameter search using the Ray Estimator, we can simply pass in Ray Tune’s tune.run() with XGBoost Ray’s train() as serializable functions …

Weights & Biases is a cloud-based service that allows you to host your experiments in a single central repository and if you have a private infrastructure, Weights & Biases can also be deployed on it.

However, when it comes to delivery, Weights & Biases isn’t always the best option.

Weights & Biases vs NeptuneBoth Neptune and Weights & Biases are hosted services and they provide experiment tracking, model management, and data versioning.

Weights & Biases vs KubeflowUsing Kubeflow pipeline or KF Serving components in Kubeflow, you can deploy machine learning models on docker, something that is missing on Weights & Biases.

Weights & Biases vs Amazon SageMaker StudioSageMaker Studio has an eas…

There are many companies that provide open source libraries containing pre-trained models and Hugging Face is one of them.

In the upcoming sections, we will be covering Hugging Face and its transformers in detail with some hands-on exercises.

Basic tasks supported by Hugging FaceBefore we learn how a hugging face model can be used to implement NLP solutions, we need to know what are the basic NLP tasks that Hugging Face supports and why do we care about them.

Fortunately, hugging face has a model hub, a collection of pre-trained and fine-tuned models for all the tasks mentioned above.

There are many models which are available in the Hugging Face model hub or can be created using multilingua…

ML software development is complex; building an ML model is one thing, improving and maintaining it, is another.

Model governance, model provenance, and model lineage tools help you in doing just that by tracking model activity, recording all changes in the data and the model, and outlining best practices for data management and disposal.

Model provenance is tightly connected to the model governance process and describes the origin of the model and the processing steps that were applied to it.

Top tools for model governance, model provenance, and model lineageSince model governance, model provenance, and model lineage are tightly interconnected, here is a unified list of tools that help ML …

While TensorFlow seems like a great tool to have in your arsenal for most deep learning tasks, most people are now preferring to switch from TensorFlow to PyTorch.

PyTorch is best for developing rapid prototypes and research projects which is why most people are choosing to transition from TensorFlow to PyTorch.

You may also like 💡 How to Keep Track of Experiments in PyTorch Using NeptuneIs it easy to switch from TensorFlow to PyTorch?

Understanding the simpler PyTorch installationPyTorch installation | SourceIn comparison to the TensorFlow installation, the PyTorch installation is much simpler.

The transition for switching from TensorFlow to PyTorch isn’t too complex because PyTorch offers…

K-means clustering | Source: AuthorRe-initialize centroidsNext, we will re-initialize the centroids by calculating the average of all data points of that cluster.

In this section, we will be building a K-means clustering algorithm from scratch using a random centroid initialization method.

May be useful Check how you can keep track of your classifiers, regressors, and k-means clustering results when using Scikit-Learn.

GMM vs K-means clustering algorithm and why K-means is so popularGMM uses probability distribution and K-means uses distance metrics to compute the difference between data points to segregate the data into different clusters.

Using techniques such as K-means Clustering, one c…

In this article, I will be discussing my top 6 model versioning tools that can greatly improve your workflow.

Types of model versioning tools Model versioning vs data versioning What tools can be used for model versioning How do these tools compare with each other?

Model versioning in a way involves tracking the changes made to an ML model that has been previously built.

Model versioning vs data versioningIn some cases, the differences between model and data versioning are quite clear.

Best tools for model versioningHere I will discuss six different tools for model versioning, outlining steps to get started and their different capabilities.

It is imperative to build a continuous feedback loop as part of any model performance management workflow.

Tooling to experiment, validate, monitor, and actively improve models helps ML practitioners consistently deploy — and maintain — higher-quality models in production.

That’s why Arize AI and Neptune AI are pleased to announce a partnership, connecting Arize’s ML observability platform with Neptune’s metadata store for MLOps.

TL;DR – ML practitioners can fine-tune model performance and actively improve models at a more granular level when using Arize and Neptune combined.

Arize’s automated model monitoring and analytics platform allow ML teams to quickly detect issues when they emerge, …

Tools and libraries used for dimensionality reduction Algorithms used for dimensionality reduction Applications Advantages and disadvantagesWhat is dimensionality reduction?

Dimensionality reduction is commonly used in data visualization to understand and interpret the data, and in machine learning or deep learning techniques to simplify the task at hand.

To gain some understanding of the relationships between these points, we can use PCA to project them to lower dimensions, like 2-D:from sklearn.decomposition import PCA pca = PCA( 2 ) projected = pca.fit_transform(digits.data) print(digits.data.shape) print(projected.shape)(1797, 64)(1797, 2)Now, let’s plot the first two principal componen…

We will use Amazon’s NoSQL serverless database DynamoDB for this as it scales pretty well and integrates with the serverless function quite easily.

Go back to the CodeBuild page for the project you created and click on Start build to test your application build.

You can now make a final commit of your application source code to ensure your pipeline works as intended.

Deploying your image classification application to AWS LambdaNow your application build is all set to be deployed to a serverless function.

To deploy it, we have to set up AWS Lambda to run the application build whenever there is a new event.

Azure Machine Learning (AML) is a cloud-based machine learning service for data scientists and ML engineers.

You’re in luck – in this article, we’re reviewing multiple alternatives to Azure Machine Learning (AML) for MLOps.

To find alternatives for AML, first, we need to analyze exactly what AML does:Experiment Tracking Model Management Model Deployment Model Lineage Model Monitoring Data Labelling1.

In G2 Crowd reviews of the Azure ML, users have mentioned that :Azure ML doesn’t meet the requirement for their specific use case; it’s not so flexible and customization can be hard.

For more data labeling tools, check also ➡️ Data Labeling Software: Best Tools for Data Labeling in 2021.

However, as data science has evolved over the decade, it has become clearer that data science involves more than just modeling.

The machine learning lifecycle, from raw data through to deployment, now relies on specialized experts including data engineers, data scientists, machine learning engineers along with product and business managers.

Thus, the definition and scope of a data scientist vs. a machine learning engineer is very contextual and depends upon how mature the data science team is.

DATA SCIENTIST MACHINE LEARNING ENGINEER Problem-solving Programming Programming Data structures Statistics Data modeling Data science Software engineering Machine learning: Supervised & Unsupervised …

This time I want to explore how deep reinforcement learning can be utilized e.g.

Continuous Control with Deep Reinforcement Learning | Source: RoboschoolMeet Humanoid.

Off-policy actor-critic methodsReinforcement Learning | Source: Sutton & Barto, Reinforcement Learning: An Introduction, 2nd editionLet’s recap: Reinforcement learning (RL) is learning what to do — how to map situations to actions — to maximize some notion of cumulative reward.

Reinforcement learning | Source: ResearchgateThe actor-critic architecture, depicted in the diagram above, divides the agent into two pieces, the Actor and the Critic.

You can read more about logging these diagnostics in Logging in Reinforcement Learni…

With the introduction of packages like transformers by huggingface, it is very convenient to train NLP models for any given task.

What methods, tips, and tricks you can use to train state-of-the-art NLP models?

Most of the options needed to quickly make comparisons are available in a couple of clicks therefore we can interactively run analysis and train models.

The field is evolving drastically and researchers are discovering new tricks every day to reliably train these models.

We hope with this article you are empowered with great techniques and tools to confidently train and track the stable state-of-the-art NLP models.

Read also 🎓 Version Control Guide for Machine Learning ResearchersTypes of version control systemsThere are three main types of version control systems:Local Version Control Systems Centralized Version Control Systems Distributed Version Control SystemsLocal Version Control SystemThis version control system consists of a local database on your computer that stores every file change as a patch (difference between files in a unique format).

Local Version Control System | Source: GitlabCentralized Version Control SystemsThis system has a central and single server that stores the different file versions.

Distributed Version Control Systems | Source: GitlabBenefits of version control systemsEffi…

Predicting paraphrased sentences: Often when we look at text data, we’re trying to understand if certain sentences are related.

This is useful when processing large amounts of text data, and you want to search for or identify text which may be related without being identical.

Predicting text data with TransformersThe datasetFor predicting text data we want to use a dataset that represents “real” queries and conversations.

But it’s always good to keep an eye on new applications like this which may be improved by new NLP models like BERT or GPT.

Models like Google’s T5 Text to Text Transformer, which was released in 2020, are examples of transformer-based models that can perform multiple task…

#imle #backpropagation #discrete Backpropagation is the workhorse of deep learning, but unfortunately, it only works for continuous functions that are amenable to the chain rule of differentiation. Since discrete algorithms have no continuous derivative, deep networks with such algorithms as part of them cannot be effectively trained using backpropagation. This paper presents a method to incorporate a large class of algorithms, formulated as discrete exponential family distributions, into deep networks and derives gradient estimates that can easily be used in end-to-end backpropagation. This enables things like combinatorial optimizers to be part of a network's forward propagation natively.…

#neurips #peerreview #machinelearning A look at the results of the 2021 NeurIPS peer review experiment. https://arxiv.org/abs/2109.09774

https://www.reddit.com/r/MachineLearning/comments/qzjuvk/discussion_neurips_2021_finally_accepted/ Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://discord.gg/4H8xxDF

BitChute: https://www.bitchute.com/channel/yannic-kilcher

LinkedIn: https://www.linkedin.com/in/ykilcher

BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financial…

#deeplearning #neuralarchitecturesearch #metalearning Deep Neural Networks are usually trained from a given parameter initialization using SGD until convergence at a local optimum. This paper goes a different route: Given a novel network architecture for a known dataset, can we predict the final network parameters without ever training them? The authors build a Graph-Hypernetwork and train on a novel dataset of various DNN-architectures to predict high-performing weights. The results show that not only can the GHN predict weights with non-trivial performance, but it can also generalize beyond the distribution of training architectures to predict weights for networks that are much larger, de…

#grafting #adam #sgd The last years in deep learning research have given rise to a plethora of different optimization algorithms, such as SGD, AdaGrad, Adam, LARS, LAMB, etc. which all claim to have their special peculiarities and advantages. In general, all algorithms modify two major things: The (implicit) learning rate schedule, and a correction to the gradient direction. This paper introduces grafting, which allows to transfer the induced learning rate schedule of one optimizer to another one. In that, the paper shows that much of the benefits of adaptive methods (e.g. Adam) are actually due to this schedule, and not necessarily to the gradient direction correction. Grafting allows for …

#mlnews #cedille #wmt Only the greatest of news from the world of Machine Learning. OUTLINE:

0:00 - Sponsor: Weights & Biases

1:50 - Cedille - French Language Model

3:55 - Facebook AI Multilingual model wins WMT

5:50 - YOU private search engine

10:35 - DeepMind's Open-Source Arnheim

12:10 - Company sued for using AI to make website more accessible

18:05 - Alibaba DAMO Academy creates 10 Trillion M6 model

21:15 - AMD MI200 Family

22:30 - State of AI report 2021

24:15 - Andrew Ng's Landing AI raises 57M

25:40 - Cerebras raises 250M

26:45 - Microsoft's Varuna: Scalable Training of Huge Models

28:15 - Laura Ruis reproduces Extrapolation Paper

29:05 - Ian Charnas' Real-Life Punchout

30:00 - Help…

#deeplearning #backpropagation #simulation More and more systems are made differentiable, which means that accurate gradients of these systems' dynamics can be computed exactly. While this development has led to a lot of advances, there are also distinct situations where backpropagation can be a very bad idea. This paper characterizes a few such systems in the domain of iterated dynamical systems, often including some source of stochasticity, resulting in chaotic behavior. In these systems, it is often better to use black-box estimators for gradients than computing them exactly. OUTLINE:

0:00 - Foreword

1:15 - Intro & Overview

3:40 - Backpropagation through iterated systems

12:10 - Connecti…

#mlnews #turing #reskin The latest and greatest from the Machine Learning world OUTLINE:

0:00 - Intro

0:15 - Sponsor: Weights & Biases Tables

3:25 - Microsoft Turing Bletchley: Universal Image Language Representation Model

6:35 - Meta AI Tactile Sensing

9:55 - AnimeGANv2

11:35 - General In-Hand Object Re-Orientation

13:05 - Does Facebook score the "Anger" Emoji too high?

17:05 - IsomorphicLabs: New Alphabet Company for Drug Discovery

18:15 - ruDALL-E: Russian DALL-E

20:40 - Image Scaling Attacks

23:25 - Azure OpenAI Service

24:10 - Neural MMO

25:40 - ArxivDOOM

26:50 - ARC Game

29:35 - ResNeXtGuesser

29:55 - Zillow loses money based on AI home price estimation

31:35 - Helpful Things

35:40 - …

#machinelearning #ardm #generativemodels Diffusion models have made large advances in recent months as a new type of generative models. This paper introduces Autoregressive Diffusion Models (ARDMs), which are a mix between autoregressive generative models and diffusion models. ARDMs are trained to be agnostic to the order of autoregressive decoding and give the user a dynamic tradeoff between speed and performance at decoding time. This paper applies ARDMs to both text and image data, and as an extension, the models can also be used to perform lossless compression. OUTLINE:

0:00 - Intro & Overview

3:15 - Decoding Order in Autoregressive Models

6:15 - Autoregressive Diffusion Models

8:35 - D…

#pathways #mlnews #ego4d Your irregular dose of Machine Learning News. OUTLINE:

0:00 - Intro

0:20 - Sponsor: Weights & Biases

2:10 - Google Introduces Pathways AI Architecture

6:30 - OpenAI trains Language Models to do High School Math

8:25 - Sam Altman says Neural Networks truly learn

9:35 - Google AI researchers frustrated with lawyers

12:10 - DeepMind RL Lecture Series 2021

12:40 - Fashion Store sells Adversarial Patches

13:15 - A viable method to remove the GIL from CPython

15:05 - BigScience Workshop releases T0

17:40 - Huggingface Hub Dataset Viewer

18:10 - Scite classifies scientific citations

19:25 - Facebook AI Ego4D dataset & challenges

21:50 - Tesla Dojo Configurable Floating Poi…

#efficientzero #muzero #atari Reinforcement Learning methods are notoriously data-hungry. Notably, MuZero learns a latent world model just from scalar feedback of reward- and policy-predictions, and therefore relies on scale to perform well. However, most RL algorithms fail when presented with very little data. EfficientZero makes several improvements over MuZero that allows it to learn from astonishingly small amounts of data and outperform other methods by a large margin in the low-sample setting. This could be a staple algorithm for future RL research. OUTLINE:

0:00 - Intro & Outline

2:30 - MuZero Recap

10:50 - EfficientZero improvements

14:15 - Self-Supervised consistency loss

17:50 - E…

#ytalks #siraj #plagiarism A conversation with Siraj Raval about his journey on YouTube, and the perils of fame. OUTLINE:

0:00 - Intro

1:30 - Welcome

3:15 - Starting out: From Economics to YouTube

13:00 - More Views: Plagiarizing Video Content

23:30 - One Step Up: Copying A Research Paper

29:15 - Was there another way?

39:00 - Clickbait Course: Make Money with Machine Learning

50:30 - Rock Bottom and the Way Forward

1:01:30 - Advice for Future Generations Siraj's Channel: https://www.youtube.com/c/SirajRaval Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://discord.…

#gtc21 #mlnews #mujoco Register to GTC'21 and Win a RTX 3090: https://nvda.ws/2Y2B5ni OUTLINE:

0:00 - Intro

0:15 - Sponsor: NVIDIA GTC'21

5:35 - DeepMind buys & Open-Sources MuJoCo

7:25 - PyTorch 1.10 Released

9:10 - Google Predicts Spreadsheet Formulas

11:25 - handtracking.io

12:25 - Cell Instance Segmentation Challenge

13:00 - Helpful Libraries

17:50 - Waymo cars keep turning into same dead-end

19:35 - BlueRiver balances tractors References:

DeepMind buys & open-sources MuJoCo

https://deepmind.com/blog/announcements/mujoco PyTorch 1.10 released

https://pytorch.org/blog/pytorch-1.10-released/

https://developer.nvidia.com/blog/cuda-graphs/ GoogleAI predicts spreadsheet formulas

https://ai.g…

#gtc21 #mlnews #mujoco Registriere für GTC'21 und gewinne eine RTX 3090: https://nvda.ws/2Y2B5ni OUTLINE:

0:00 - Intro

0:15 - Sponsor: NVIDIA GTC'21

6:10 - DeepMind kauft & Open-Sourct MuJoCo

9:05 - PyTorch 1.10 Veröffentlicht

11:25 - Google Lernt Spreadsheet Formeln

14:15 - handtracking.io

15:25 - Zellinstanzsegmentierungswettbewerb

16:15 - Hilfreiche Bibliotheken

23:15 - Waymo autos verirren sich alle in der selben Sackgasse

24:50 - BlueRiver balanciert Traktoren References:

DeepMind kauft & open-sourct MuJoCo

https://deepmind.com/blog/announcements/mujoco PyTorch 1.10 veröffentlicht

https://pytorch.org/blog/pytorch-1.10-released/

https://developer.nvidia.com/blog/cuda-graphs/ GoogleAI sa…

#aiia #ai #art A trip report from the AiiA Festival in Geneva organized by the ImpactAI foundation. OUTLINE:

0:00 - Intro

1:50 - Laura Tocmacov: The Festival

4:10 - Timothy O'Hear: The Tech

6:50 - Jonathan O'Hear: The Robot

11:50 - Cléa Chopard: The Artist

17:45 - Final Words Website: https://aiiafestival.org/en/ Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://discord.gg/4H8xxDF

BitChute: https://www.bitchute.com/channel/yannic-kilcher

Minds: https://www.minds.com/ykilcher

Parler: https://parler.com/profile/YannicKilcher

LinkedIn: https://www.linkedin.com/in/ykilc…

#gpt3 #knowledge #symbolic Symbolic knowledge models are usually trained on human-generated corpora that are cumbersome and expensive to create. Such corpora consist of structured triples of symbolic knowledge. This paper takes a different approach and attempts to generate such a corpus by prompting GPT-3. Results show that clever prompting, combined with targeted small critic models trained on human ratings can outperform both human-generated data, as well as the teacher model (GPT-3) itself. The results of this paper give a general recipe for automatically building corpora for various NLP tasks by extracting samples from large language models. OUTLINE:

0:00 - Intro & Overview

2:30 - Spons…

This video provides an overview of Graph Embeddings and how PyTorch-BigGraph enables learning graph embeddings for very large graphs. The key challenge with this is you would need a lot of memory to store the vectors for each node in a large graph. To solve this, BigGraph uses novel partitioning, distributed execution, and negative sampling algorithms. I hope this is a decent introduction to graph embeddings and PyTorch-BigGraph, really excited about the upcoming release of the Wikidata Weaviate web demo! PyTorch-BigGraph (blog post) - https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs/

PyTorch-BigGraph (paper) - https://arxiv.org/abs…

Subscribe to the Vector Podcast: https://www.youtube.com/channel/UCCIMPfR7TXyDvlDRXjVhP1g Not All Vector Databases are Equal: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696 Get started with Weaviate!

https://www.semi.technology/developers/weaviate/current/getting-started/quick-start.html Chapters

0:00 Introduction

2:55 What inspired your interest?

6:24 Weaviate Modules - Search + QA and more

7:40 BERT + Elasticsearch

13:42 FAISS and recent developments

17:37 Mixed Precision

18:21 Symbolic Filters and Vector Search

24:34 Approximate Nearest Neighbor Benchmarks

27:38 ANN vs. Information Retrieval

32:58 Robus…

This is a prelude to the Weaviate Podcast (moving to SeMI YouTube).

Etienne Dilocker and I discuss the Weaviate Slack, the Weaviate console for Dataset Interfaces, HNSW, and many more slack topics such as encoding longer than 512 tokens for vector similarity search. Thank you so much for watching, please head over to the SeMI YouTube for future episodes and check out the Weaviate slack if you would like your questions answered here! Weaviate Slack: https://join.slack.com/t/weaviate/shared_invite/zt-goaoifjr-o8FuVz9b1HLzhlUfyfddhw

Weaviate Web Demo: https://www.semi.technology/developers/weaviate/current/getting-started/quick-start.html

ANN Benchmarks: http://ann-benchmarks.com/

Etienne Dilo…

There has been a new breakthrough in using Data Augmentation in Deep Learning. Researchers from NVIDIA, Caltech, UT Austin, and Arizona State have published AugMax. The acronym AugMax communicates the use of Data Augmentation and Maximizing the Diversity and Difficulty of Augmented Examples. AugMax is basically free lunch in Deep Learning meaning that it is a very strong technique to add to any existing Deep Learning workflow. But more particularly, Data Augmentation has been heavily studied with Image Data in Computer Vision. AugMax is the sequel to AugMix.

This predecessor tree of research in Data Augmentation is roughly AutoAugment to RandAugment to AugMix, and now AugMax. AugMax further…

This video walks through some example queries to help you get started with Weaviate's GraphQL API. If you have any questions, ideas, or want advice on your personal use case, please leave it in the comments! Stay tuned for a tutorial on using a custom dataset with Weaviate! Quick Start Tutorial: https://www.semi.technology/developers/weaviate/current/getting-started/quick-start.html

How Weaviate's GraphQL API was designed: https://hackernoon.com/how-weaviates-graphql-api-was-designed-t93932tl Chapters

0:00 Neurosymbolic Systems

0:55 Query 1

2:00 Query 2

2:33 Query 3

3:27 Query 4 - Symbolic Filters!

4:16 Query 5

5:09 Query 6 - Neural Search!

6:24 Query 7 - Neurosymbolic Query!

7:43 Query 8

8…

This video presents Weaviate Vector Search Engine! Please leave a comment if you have any ideas or questions! Weaviate Documentation: https://www.semi.technology/developers/weaviate/current/

SeMI Technologies on YouTube: https://www.youtube.com/channel/UCJKT6kJ3IFYybWnL7jbXxhQ/videos Chapters

0:00 Overview of Chapters

5:16 Semantic Similarity

7:55 Symbolic Queries

9:45 Vector + Symbolic Search

10:23 Weaviate Modules

13:44 Demo Dataset and GraphQL

15:12 Integration Project

16:16 Research Ideas

19:18 Connect with us!

Vector Search Engines are powering the the next generation of search. Instead of relying on things like BM25 or TF-IDF representations, we use neural representations to compare semantic similarity. Dot products to calculate the similarity between all vectors would be very time consuming. Hierarchical Navigable Small World Graphs (HNSW) are a cutting-edge new algorithm used by Weaviate to speed up this search. This podcast explores HNSW, Neurosymbolic search and filtering, and many more! I hope you find this interesting, happy to answer any questions left on the YouTube video! Check out this Introduction to Weaviate: Very well organized, no headaches getting started!

https://www.semi.technol…

Researchers from the University of Washington, Columbia University, Open AI, the Allen Institute of Artificial Intelligence, and Toyota Research have teamed up to present a new method for fine-tuning these pre-trained models such as GPT-3, BERT, DALL-E, EfficientNet, or CLIP for application specific datasets. The key insight is that as you fine-tune these models, you gain in-distribution accuracy, but sacrifice the zero-shot flexibility, or out-of-distribution generalization, of these pre-trained “foundation” models. The authors present Weight-Space Ensembling, where you take a linear interpolation between the weights of the zero-shot and fine-tuned model to make new inference. This achieve…

Researchers from the University of Washington, Columbia University, Open AI, the Allen Institute of Artificial Intelligence, and Toyota Research have teamed up to present a new method for fine-tuning these pre-trained models such as GPT-3, BERT, DALL-E, EfficientNet, or CLIP for application specific datasets. The key insight is that as you fine-tune these models, you gain in-distribution accuracy, but sacrifice the zero-shot flexibility, or out-of-distribution generalization, of these pre-trained “foundation” models. The authors present Weight-Space Ensembling, where you take a linear interpolation between the weights of the zero-shot and fine-tuned model to make new inference. This achieve…

AI Weekly Update Notion Page: https://ebony-scissor-725.notion.site/AI-Weekly-Update-September-8th-2021-a2119851b5b74470b4971d064665e77e

New AI Weekly Update GitHub Repo: https://github.com/CShorten/AIWeeklyUpdates I am looking for collaborators for two survey papers on Text-to-Image Generation and Data Augmentation Controllers. If you are interested in helping out and being a co-author of either paper, please send me a quick overview of what you think about the topic to cshorten2015@fau.edu! Content Links in the Video:

Challenges in Generalization in Open Domain Question Answering: https://arxiv.org/pdf/2109.01156.pdf

Hurdles to Progress in Long-Form Question Answering: https://arxiv.org/p…

Notion Link: https://ebony-scissor-725.notion.site/AI-Weekly-Update-August-7th-2021-3b21331c5c6a45e1a5638955dda7923c Chapters:

0:00 Introduction

0:06 Open-Ended Learning

2:38 Persistent Reinforcement Learning

3:34 Domain-Matched Pre-training for Retrieval

4:22 Growing Knowledge Culturally

5:06 Language Grounding with 3D Objects

6:03 Pre-train, Prompt, and Predict

6:32 QA Dataset Explosion

7:32 ProtoTransformer

9:32 Don’t sweep your Learning Rate under the Rug

10:34 AAVAE

11:40 Domain-Agnostic Contrastive Learning

12:43 Dataset Distillation

14:04 Pointer Value Retrieval

16:01 Go Wider Instead of Deeper

16:52 Geometric Deep Learning on Molecules

18:37 Simulation Framework for Label Noise

19:5…

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Follow Katherine Lee on Twitter @katherine1ee

Twitter Thread Link: https://twitter.com/katherine1ee/status/1415496898241339400 Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thanks for watching! Please Subscribe!

Take a look at the full playlist (really): https://www.youtube.com/watch?v=fJWnA4j0_ho&list=PLnQX-jgAF5pTkwtUuVpqS5tuWmJ-6ZM-Z

Blog post with more details: https://3b1b.co/some1-results

Thanks, as always, to the supporters of this channel for helping to make this whole project possible: http://3b1b.co/thanks ------------------ Typo at 2:00, it should read "Burkard Polster" Videos and posts mentioned here. That weird light at the bottom of a mug — ENVELOPES

https://youtu.be/fJWnA4j0_ho Hiding Images in Plain Sight: The Physics Of Magic Windows

mattferraro.dev/posts/caustics-engineering The Beauty of Bézier Curves

https://youtu.be/aVwxzDHniEw What Is The Most Complicated Lock Pattern?

https:/…

How the right question about Newton's method results in a Mandelbrot set.

Video on Newton's fractal: https://youtu.be/T_S2j5GaLRQ

Special thanks: https://3b1b.co/lessons/newtons-fractal#thanks Extra special thanks to Sergey Shemyakov, of Aix-Marseille University, for helpful conversations and for introducing me to this phenomenon. ------------------ Introduction to Fatou sets and Julia sets, including a discussion of Montel's theorem and its consequences:

http://www.math.stonybrook.edu/~scott/Papers/India/Fatou-Julia.pdf Numberphile with Ben Sparks on the Mandelbrot set:

https://youtu.be/FFftmWSzgmk Excellent article on Acko.net, from the basics of building up complex numbers to Julia sets.…

Who knew root-finding could be so complicated?

Early view of the next part: https://www.patreon.com/3blue1brown

An equally valuable form of support is to simply share the videos. ------------------ On fractal dimension:

https://youtu.be/gB9n2gHsHN4 Mathologer on the cubic formula:

https://youtu.be/N-KXStupwsc Some of the videos from this year's Summer of Math Exposition are fairly relevant to the topics covered here. Take a look at these ones, The Beauty of Bézier Curves

https://youtu.be/aVwxzDHniEw The insolubility of the quintic:

https://youtu.be/BSHv9Elk1MU The math behind rasterizing fonts:

https://youtu.be/LaYPoMPRSlk --- These animations are largely made using a custom python library,…

Learn more and submit: https://3b1b.co/SoME1

Podcast/New channel: https://youtu.be/C-i4q-Xlnis

↓↓Things referenced through the video↓↓ Join the discord channel:

https://discord.gg/SRTErdZ9 James Schloss:

https://www.youtube.com/user/LeiosOS Free will theorem:

https://www.ams.org/notices/200902/rtx090200226p.pdf Kolmogorov complexity and primes:

https://people.cs.uchicago.edu/~fortnow/papers/kaikoura.pdf Tadashi Tokieda talk:

https://youtu.be/tQQ3oiB32GI Boarbarktree:

https://www.youtube.com/channel/UCFeIEAkqvS4fJMTwUtF4OFw Mathologer:

https://youtu.be/N-KXStupwsc Manim:

https://github.com/3b1b/manim Manim Community edition:

https://github.com/ManimCommunity/manim/ Reanimate:

https://github.…

How to write the eigenvalues of a 2x2 matrix just by looking at it.

Thanks to Tim for the jingle: https://www.youtube.com/acapellascience

Help fund future projects: https://www.patreon.com/3blue1brown​

An equally valuable form of support is to simply share the videos.

Special thanks to these supporters: https://3b1b.co/quick-eigen-thanks Introduction to eigenvectors and eigenvalues:

https://youtu.be/PFDu9oVAE-g Lockdown math lecture talking about the mean product formula:

https://youtu.be/MHXO86wKeDY Timestamps:

0:00 - Background

4:53 - Examples

10:24 - Relation to the characteristic polynomial

12:00 - Last thoughts ------------------ These animations are largely made using a custom python …

General exponentials, Love, Schrödinger, and more.

Home page: https://www.3blue1brown.com

Brought to you by you: https://3b1b.co/thanks ------------------

The Romeo-Juliet example is based on this essay by Steven Strogatz:

http://www.stevenstrogatz.com/essays/loves-me-loves-me-not-do-the-math The book shown at the start is Vladimir Arnold's (excellent) textbook on ordinary differential equations.

https://amzn.to/3dtXSwj Need a review of ordinary powers of e?

https://youtu.be/m2MIpDrF7Es Or of linear algebra?

https://youtu.be/kYB8IZa5AuE Timetable

0:00 - Definition

6:40 - Dynamics of love

13:17 - General equation

20:03 - On general rotations

22:11 - Visualizing with flow ------------------

C…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Optimal Stroke Learning with Policy Gradient Approach for Robotic Table Tennis" is available here:

https://arxiv.org/abs/2109.03100

https://www.youtube.com/watch?v=SNnqtGLmX4Y ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Ch…

❤️ Check out Weights & Biases and say hi in their community forum here: https://wandb.me/paperforum 📝 The paper "Unified particle system for multiple-fluid flow and porous material" is available here:

https://cronfa.swansea.ac.uk/Record/cronfa57521

https://dl.acm.org/doi/10.1145/3450626.3459764 Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://discordapp.com/invite/hbcTJu2 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamant…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers 📝 The paper "Interactive Liquid Splash Modeling by User Sketches" is available here:

https://web.cse.ohio-state.edu/~wang.3602/Yan-2020-ILS/Yan-2020-ILS.pdf

https://web.cse.ohio-state.edu/~wang.3602/publications.html 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Mich…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "ADOP: Approximate Differentiable One-Pixel Point Rendering" is available here:

https://arxiv.org/abs/2110.06635 ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas,…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Image Super-Resolution via Iterative Refinement " is available here:

https://iterative-refinement.github.io/

https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michae…

❤️ Check out Cohere and sign up for free today: https://cohere.ai/papers 📝 The paper "Large Steps in Inverse Rendering of Geometry" is available here:

https://rgl.epfl.ch/publications/Nicolet2021Large 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramse…

❤️ Train a neural network and track your experiments with Weights & Biases here: http://wandb.me/paperintro 📝 The paper "A Constraint-based Formulation of Stable Neo-Hookean Materials" is available here:

https://mmacklin.com/neohookean.pdf 📝 The paper "Gaussian Material Synthesis" (with the pseudocode) is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Da…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "GPU-based Simulation of Cloth Wrinkles at Submillimeter Levels" is available here:

https://web.cse.ohio-state.edu/~wang.3602/publications.html

https://dl.acm.org/doi/abs/10.1145/3450626.3459787 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrech…

❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd 📝 The paper "Medial IPC: accelerated incremental potential contact with medial elastics" is available here:

https://yangzzzy.github.io/PDF/medial_IPC_SIG21.pdf

https://dl.acm.org/doi/10.1145/3450626.3459753 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael …

❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "High-order Differentiable Autoencoder for Nonlinear Model Reduction" is available here:

https://arxiv.org/abs/2102.11026 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Michael Tedder, Nikhil Velpanur, Owen Campbell-Mo…

❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "A glacier–ocean interaction model for tsunami genesis due to iceberg calving" is available here:

https://www.nature.com/articles/s43247-021-00179-7 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen …

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "DiSECt – A Differentiable Simulation Engine for Autonomous Robotic Cutting" is available here:

https://diff-cutting-sim.github.io/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skar…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning" is available here:

https://attentionneuron.github.io/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/latentspace/published-work/The-Science-of-Debugging-with-W-B-Reports--Vmlldzo4OTI3Ng 📝 The paper "DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks" is available here:

https://depthoraclenerf.github.io/ Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://discordapp.com/invite/hbcTJu2 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Ra…

❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "Ships, Splashes, and Waves on a Vast Ocean" is available here:

http://computationalsciences.org/publications/huang-2021-vast-ocean.html 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moo…

Data Fest Online 2021 https://fest.ai/2021/

ML Art track https://ods.ai/tracks/ml-art-df2021 Speaker introduces himself in Russian, and then presents the material in English.

Data Fest Online 2021 https://fest.ai/2021/

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021 Modern Digital Advertising Platforms Leverage Machine Learning and AI to help Advertisers to achieve their goals. Being managed by humans, Advertising technological potential is often remains under-utilised as Humans tend to follow stereotypes and rely on “gut feeling” when making decisions. Understanding of the underlying principles behind “Googles and Facebook’s of our world” therefore becomes a crucial skill a modern marketer needs to acquire to stay relevant. In this talk, we will shed the light into the complex Digital Advertising ecosystem and will show you techniques, such a…

Data Fest Online 2021 https://fest.ai/2021/

ML REPA track https://ods.ai/tracks/ml-repa-df2021 Presentation: https://yadi.sk/i/a25573AB8IZUyw In this video we will analyse the requirements for modern MLOps and the main trends: Human-Centered AI, Fairness, Explainability, Model Monitoring, Human Augmented AI

Data Fest Online 2021 https://fest.ai/2021/

RL + Catalyst track https://ods.ai/tracks/catalyst-and-rl-df2021

Data Fest Online 2021 https://fest.ai/2021/

RL + Catalyst track https://ods.ai/tracks/catalyst-and-rl-df2021

Data Fest Online 2021 https://fest.ai/2021/

RL + Catalyst track https://ods.ai/tracks/catalyst-and-rl-df2021

ML Training 2019 Artur Kuzin tells about his participation in the competition Driven Data Hakuna Ma-data: Identify Wildlife on the Serengeti with AI for Earth. He took second place. In this video, you will find out: - Overview of a training procedure on Imagenet1k from scratch

- Implementation Details of Hacks & Tricks

- The specialty of working with JPEG pictures and resize in different frameworks Presentation - https://gh.mltrainings.ru/presentations/Kuzin_DrivenDataHakuna.pdf

ML Training 2019 Yury Bolkonsky and Andrei Dukhounik tell about their participation in Kaggle Understanding Clouds from Satellite Images. The team got a silver medal. In this video you will find out:

- Thresholding is an evil, believe in your classification models

- Why you should always use modern best practices

- Why it is not recommended to use postprocessing without local validation Presentation - https://gh.mltrainings.ru/presentations/Bolkonsky_KaggleUnderstandingClouds.pdf

ML Training 2019 Yury Bolkonsky tells about his participation in Kaggle ASHRAE - Great Energy Predictor III. His team won a gold medal. In this video you will find out:

- How to create timestamp features

- Do you need to use a leak if it is noisy?

- Leak validation for the best solution

ML Training 2019 Denis Vorotyntsev won AutoSeries - AutoML competition on time-series regression. In his presentation, he talks about the competition organization, his final solution, and solutions of other top placed participants. In this video, you will find out:

- How AutoML competition differs from most common Kaggle-alike and why you should try them

- Features engineering approach for time-series tasks when you have no idea about domain

- Why validation split should emulate train-test split

- Why you should always check the code of top participants and how small bugs might drop your score Presentation - https://gh.mltrainings.ru/presentations/Vorotyntsev_CodalabAutoML.pdf

Сегодня на семинаре будет доклад на тему Documenting research projects: How to structure your ideas and make yourself understood. Мы расскажем, как писать тех. документацию, чтобы было понятно читателям: основные приемы, которые упрощают процесс изложения мыслей и делают его более результативным. Также немного остановимся на примерах из ридми и научных статей, а затем поделимся советами по улучшению навыков английского языка. После презентации будет сессия вопросов и ответов. Докладчик: Елена Тихомирова.

Графовые нейронные сети и молекулярные отпечатки пальцев являются наиболее частыми подходами предсказанию свойств малых молекул. Однако развитие и повышение доступности инструментов для обработки естественного языка, в том числе трансформеров, позволяет предпринять попытку использования этих методов для предсказания свойств малых молекул с помощью создания модели ChemBERTa. На семинаре мы рассмотрим статью "ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction", обсудим применявшиеся методы и сравним результаты авторов с результатами текущих state-of-the-art моделей. Докладчики: Михаил Лебедев.

В статье поднимается следующий вопрос: Как искусственный интеллект может помочь разработчикам программного обеспечения лучше выполнять свою работу?

Подробно будут разобраны следующие рутинные задачи, которые встают перед программистом каждый день:

- поиск кода на естественном языке

- рекомендации кода - автоматическое исправление ошибок Разберем не только реализации каждой из задач, но и способ их интеграции в среду, а так же самое главное - реакцию программистов после интеграции.

Если останется время, будет более детально разобран алгоритм поиска кода на естественном языке, который в данный момент находится в стадии разработки в JetBrains. Докладчики: Ольга Лавриченко.

Несмотря на многочисленные прорывы, достигнутые с помощью обучения с подкреплением (RL), решение сред с разреженными наградами остается актуальной задачей, требующей нетривиальной эксплорации. Обучение на демонстрациях (LfD) пытается решить эту проблему, направляя эксплорацию агента ближе к состояниям, продемонстрированным экспертом. Естественно, преимущества этого подхода зависят от качества демонстраций, которое редко бывает оптимальными. Современные алгоритмы LfD не обладают доказуемой устойчивостью к неоптимальным демонстрациям и вводят дополнительные гиперпараметры для контроля влияния демонстраций. Чтобы решить эти проблемы, мы берем за основу RL алгоритм self-imitation learning (SIL)…

В последнее время трансформеры доминируют в задачах NLP и CV. Однако, они проигрывают многим простым GNN-архитектурам на популярных бенчмарках. Авторы адаптировали архитектуру трансформера для эффективной обработки молекулярных графов, что позволило им получить SoTA-результаты на недавнем OGB Large-Scale Challenge в graph-level задаче, а также на бенчмарк-датасетах PCBA, HIV и ZINC. На семинаре мы:

- обсудим GNN и их связь с трансформерами

- узнаем, как эффективно адаптировать трансформер для графов (positional encoding, атрибуты ребер и т.д.)

- сравним результаты Graphormer-а с результатами похожих моделей Докладчик: Дмитрий Леонов.

Авторы представляют end2end модель, принимающую на вход зашумленный цифровой сигнал и возвращающую его чистое представление. Модель можно эффективно запускать на CPU, а ее использование перед запуском ASR позволяет снизить WER при распознавании речи практически в два раза. На семинаре обсудим истоки архитектуры модели, пайплайн зашумления аудиозаписей для ее обучения, а также детали валидации. Докладчик: Дарья Дятлова.

В наши дни аукционы широко применяются в различных сферах для организации продаж товаров и услуг. В связи с этим, разработка эффективного механизма аукциона, который бы максимизировал прибыль и мотивировал участников действовать в соответствии с их предпочтениями, является актуальной задачей. Тем не менее, спустя 30-40 лет исследований, данная задача не решена даже для случая с двумя и более товарами. На семинаре мы рассмотрим статью «Optimal Auctions through Deep Learning», в которой авторы используют инструменты глубокого обучения для проектирования оптимальных аукционов. Они моделируют аукцион как многослойную нейронную сеть, определяют оптимальный дизайн аукциона как проблему ограниченн…

Геном представляет собой сложную систему взаимодействий функциональных элементов разных уровней организации - самой последовательности ДНК, мотивов, трехмерной структуры, элементов эпигенетического кода и кода вторичных структур ДНК. С помощью методов нейросетевого глубинного обучения стало возможным агрегирование информации о функциональных элементах разных уровней клеточной организации - геномики, эпигеномики, протеомики, метаболомики - и других “омик”, с целью предсказания функциональных элементов, для которых эксперименты либо не достигли нужного качества, либо отсутствуют. В докладе будет рассказано о методах глубинного обучения, разрабатываемых в международной лаборатории биоинформати…

На семинаре мы рассмотрим задачу перевода вопроса на естественном языке к базе данных в выполнимый запрос (например, на языке SQL). Многие исследователи предлагают решать ее с помощью нейросетей, которые обучаются на данных, содержащих базы, вопросы к ним и соответствующие правильные запросы. Однако собирать такую разметку сложно, так как нужно, чтобы аннотаторы знали язык запросов для их написания. Я расскажу, как с помощью промежуточных представлений можно отказаться от использования разметки с запросами для обучения, но при этом сохранить возможность модели генерировать выполняемые запросы. Статья: SPARQLing Database Queries from Intermediate Question Decompositions, EMNLP 2021, https://…

Сегодня мы разберем статью, в которой авторы решают классическую задачу генерации документации для не очень классического представления кода - для Jupyter ноутбуков. В рамках этой статьи авторы сначала проводят анализ того, что является хорошо задокументированным ноутбуком, предлагают три различных способа автоматизировать документацию и проводят user study. В итоге авторы приходят к двум интересным выводам: документация в data science используется по-другому (в отличие от обычной разработки) и что совместная генерация документации человеком и алгоритмом получается очень продуктивной. Докладчик: Сергей Титов.

В нашей работе мы хотим использовать достижения последних 30-и лет когнитивной психологии во благо разработки программного обеспечения. Мы предполагаем, что многие эффекты, построенные на решении различных когнитивных задач могут быть перенесены на задачи программирования и быть использованы для повышения удобства и эффективности написания кода. На этом семинаре мы представим наш подход, потенциальные эффекты для проверки и подробно разберем наше пилотное исследование построенное по этой схеме. Этим летом мы провели исследование, в котором определили привычные ошибки в написании кода как результат когнитивной ригидности — ментальной установки. В исследовании мы проверяли эффективность техни…

Вероятно, вы уже что-то слышали про диффузные модели и видели провокационные названия статей типа "Diffusion Models Beat GANs on Image Synthesis". Все так. Диффузные модели это относительно новый класс моделей, которые показывают sota результаты в задачах оценки плотности распределения и генерации данных. И да, на некоторых задачах генерации изображений они показывают результаты лучше, чем GAN'ы. Однако, скорее всего, вы еще не успели узнать подробности. Но вы сможете сделать это на семинаре! Во вторник мы разберем стандартные диффузные модели и посмотрим на некоторые более поздние обобщения. Поймем, как обучать эти модели, оценивать с их помощью плотность распределения, генерировать данные…

На сегодняшний день существует множество глубоких генеративных моделей, позволяющих создавать реалистичные изображения в высоком разрешении. Но во многих приложениях этого бывает недостаточно: иногда требуется уметь изменять положение и внешний вид объектов на изображении, а сам процесс генерации должен быть контролируемым. Авторы статьи «GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields» предлагают новую генеративную модель, представляющую изображения как 3D сцены, и учитывающую их композиционный характер. Модель сочетает в себе представление объектов как генеративных нейронных полей с последующим объемным и нейронным рендерингом, что позволяет ей работать быст…

00:00:00 QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

00:22:58 Graph Attention Networks with Positional Embeddings QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning Во многих областях обучения с подкреплением возникает необходимость

обучить сразу несколько агентов действовать в общей среде. В таких задачах важно

понять, как оценивать вклады каждого из агентов в достижение общей цели. Авторы

статьи “QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement

Learning” предложили новый алгоритм QMIX, который способен решать более широкий

класс задач по сравнению со своими предшественниками. …

В последнее время особенно остро стоит вопрос воспроизводимости научных результатов. Недавние исследования показывают, что кроме неполноты данных и случайных или злонамеренных ошибок в методологии (p-хакинг, множественное сравнение) на результаты исследования также влияют субъективные решения, принимамые учёными в ходе работы. Для детального изучения этого феномена авторы статьи “Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis” попросили 19 экспертов со всего мира проверить истинность двух одинаковых гипотез на одинаковом наборе данных и описать процесс принятия решений. Результаты независимых ис…

Лекция посвящена сверточным нейросетям. Дается определение сверточных слоев, расматриваются их вариации (паддинг, варианты с пропуском, depthwise-separable и 1-by-1 convolutions). Обсуждается обратное распространение через сверточные слои (а также через max pooling слой). Во второй половине лекции рассматриваются популярные архитектуры сверточных нейросетей для классификации изображений (LeNet, AlexNet, VGGNet, Inception, Resnet, ResNext, MobileNet, EfficientNet).

Последняя лекция курса посвящена применению нейросетей для задач 3D-реконструкции и представления 3D-сцен. Первая половина лекции посвящена задаче восстановления геометрии. Рассматриваются стереосопоставление, монокулярная оценка глубины, нейросети, восстанавливающие облака точек, а также объекты в неявном представлении. Во второй половине лекции рассматриваются системы, позволяющие синтезировать новые виды сцены. Рассматриваются системы, основанные на волюметрическом ренедеринге и неявных представлениях (NeRF и подобные), системы с нейротекстурированными триангулированными представлениями и точечным представлением геометрии. Дается краткий обзор пакетов для нейрорендеринга.

Рассматриваются основные алгоритмы для оптимизации функций потерь в глубинном обучении, а именно стохастический пакетный градиентный спуск и его модификации. Особое внимание уделяется градиентному спуску с моментом. Обсуждается так же пакетная нормализация (batch normalization).

В лекции описываются латентные порождающие модели на основе нейросетей, обучающиеся без учителя. В начале рассматривается модель Generative Latent Optimization (GLO), далее рассматриваются автокодировщики и их применение, включая варационные автокодировщики. Во второй половине лекции рассматриваются нормализующие потоки, а также вводятся противоборствующие сети (generative adversarial networks).

Лекция посвящена современным вариациям противоборствующих нейросетей и обучению с дискриминаторными функциями потери. Рассматриваются как продвинутые варианты латентных моделей (StyleGAN1, StyleGAN2), так и применение дискриминаторных функций потерь в задачах трансляции изображений и им подобным. Рассматриваются основные паттерны, применяемые при построении генераторов. Рассматриваются основные применения этих технологий, такие как нейросинтезаторы реалистичных изображений и нейроаватары.

В лекции рассматриваются нейросети, обрабатывающие изображения (на примере повышения разрешения), и нейросети, синтезирующие изображения. Рассматриваются функции потери (включая перцептивные -- perceptual losses, текстурные функции потери). Рассматриваются задачи синтеза текстур и стилизации изображений. Обсуждается модуль адаптивной instance нормализации и его применение.

В лекции дается обзор применений сверточных нейросетей в компьютерном зрении. Рассматриваются задачи семантической и instance сегментации, обнаружение объектов, распознавание лиц/людей. В каждом случае рассматриваются формулировка задачи, основные архитектурные паттерны для нейросетей, решающих данные задачи, популярные архитектуры. В конце обсуждаются последние работы по контрастивному предобучению нейросетей без учителя.

В лекции дается неформальное определение глубокого обучения, очень кратко освещаются история глубокого обучения, обсуждается алгоритм обратного распространения ошибки, обсуждается концепция слоев сети. В конце лекции очень кратко обсуждаются биологические нейросети.

Лекция посвящена представлениям данных, выучиваемым сверточными нейросетями. Рассматриваются разные способы визуализации и анализа подобных представлений. Обсуждаются атаки на нейросети, а также генерация искусственных изображений-иллюзий для нейросетей. Обсуждается способность больших сверточных нейросетей к переобучению под произвольные выборки. Заключительная часть лекции посвящена трансферу знаний с применением сверточных нейросетей.

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

ML in Marketing hub: https://ods.ai/hubs/ml-in-marketing

Телеграм-канал https://t.me/mlinmarketing Спикер: Иван Максимов, Data Science Team Lead at Delivery Club Многие аналитики для ускорения А/В тестов в первую очередь используют достаточно сложные статистические приемы (например, CUPED). Однако существует огромное множество более простых и эффективных способов ускорить А/В тесты. Мы обсудим 13 таких способов: от улучшения процесса дизайна теста до применения стат критерия и финального принятия решения о выкатке фичи. А также оценим потенциальный trade-off эффект-затраты от внедрения каждого из способов. Что было в докладе:

00:00 начало видео

01:18 сразу о результатах

02:07 содержание док…

ODS Course Fest #1 https://datafest.ru/course/ Полное расписание конференции доступно на ODS.AI: https://ods.ai/events/halloween2021 Курсы ODS: https://ods.ai/tracks/groups/courses Зарегистрироваться и получить доступ ко всем курсам и мероприятиям сезона: https://ods.ai/events/course_fest_1 Вступить в сообщество: https://ods.ai/ Соцсети Data Fest & Course Fest: https://t.me/datafest

https://vk.com/datafest

ML in Marketing хаб: https://ods.ai/hubs/ml-in-marketing

Телеграм-канал https://t.me/mlinmarketing *******ODS HALLOWEEN*******

Регистрация: https://ods.ai/events/halloween2021 Спикер: Андрей Кузнецов, Machine Learning Engineer в Одноклассники, преподаватель в MADE, автор статей на хабр В докладе: Расскажу о том, что было в этом году на RecSys, какие были интересные статьи, расскажу подробнее про некоторые из них:

- предсказание пользовательских кликов на управляющие элементы на мобилках от Google Research с трансформерами под капотом

- решение проблем повторяющегося потребления и динамической доступности айтемов на примере twitch

- разбор честности экспериментов в SOTA статьях при использов…

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021

Телеграм-канал https://t.me/mlinmarketing Спикер: Евгений Ключников, Data Engineer at GetYourGuide

Мы активно используем а/б тестирование для оценки новых продуктов и идей; в некоторые дни у нас запущено до 70 экспериментов от разных команд одновременно. В докладе я расскажу, как мы организовали такую систему технически, где секрет её масштабируемости, что ломается чаще всего и почему не все всегда ею довольны. Затронем тему документации, обучения, неправильного дизайна, расскажу, как сломать эксперимент так, что этого никто и не заметит. Что было в докладе:

00:00 начало видео

03:07 что такое А/Б эксперимент

06:07 для чего м…

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021

Телеграм-канал https://t.me/mlinmarketing Спикер: Денис Соловьев, Data Analyst at Ciklum Рассмотрим преимущества создания маркетинговой аналитики с использованием облачных технологий. Поговорим о том, на что стоит обратить внимание перед построением решения. Посмотрим на примеры аналитических архитектур для решения маркетинговых задач. Разберём организацию DWH на базе Google BigQuery. Затронем BigQuery ML и какие задачи можно решать с его помощью. Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021

Телеграм-канал https://t.me/mlinmarketing Спикер: Егор Борисов, Lead DS at DataWise Больше от автора:

- статья на хабр (https://habr.com/ru/company/ods/blog/572264/)

- ссылка на гитхаб (https://github.com/egorborisov/jobs_article) По мотивам статьи на хабр. Обзор рынка Data Science по данным из чатов ODS. Спрос на специалистов растет, или рынок уже насытился, какие технологии теряют, а какие набирают популярность, размер зарплатных вилок и от чего они зависят? Авторы исследования поделятся подходом, результатами и своими мыслями. Будет полезно каждому, кого волнует какие вакансии востребованы сегодня, куда и как расти. Соцсе…

Презентация: https://docs.google.com/presentation/d/1XmM9-WHl6XhIx3VQgvm-qoshCXtl9L4I1A-ykK6R9dg/edit#slide=id.ge9979d1b50_5_0 Вступить в сообщество: https://ods.ai/ Соцсети ML Trainings

VK: https://vk.com/mltrainings

Telegram: https://t.me/mltrainings 0:00 - Intro

0:25 - Постановка задачи и метрика

1:21 - Валидация

1:42 - Аугментации

1:57 - Пайплайн классификации

2:50 - Как обучали классификацию

5:20 - Детекция

7:51 - Что не сработало

8:58 - Решение топ1 и топ2

10:09 - Outro

Разбор решения по определению сложности и удобочитаемости текстов 0:00 Intro

0:20 Постановка задачи, метрика

1:27 Какие были данные?

2:00 Традиционные подходы

2:37 Фичи

3:42 Решение через Spacy

4:58 Решение через Трансформеры

11:33 Итоговый ансамбль

11:50 Альтернативный лосс

12:33 Подведение итогов Презентация: https://disk.yandex.ru/i/SJeX2so0bbYZ-A Вступить в сообщество: https://ods.ai/ Соцсети ML Trainings

VK: https://vk.com/mltrainings

Telegram: https://t.me/mltrainings

Разбор решения по выявлению аномальных оптических и электромагнитных сигналов в наборе данных 0:00 Intro

1:20 Постановка задачи

2:00 Целевая метрика

2:48 Анализ лидерборда

3:35 Лики

4:14 Решение. Что зашло?

7:46 Что не зашло?

9:12 Отличие от топ1 решения

13:28 Фишки в других решениях

17:57 Подведение итогов Презентация: https://disk.yandex.ru/i/jTwGGLGmIAY7bg Вступить в сообщество: https://ods.ai/ Соцсети ML Trainings

VK: https://vk.com/mltrainings

Telegram: https://t.me/mltrainings

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021

Телеграм-канал https://t.me/mlinmarketing Спикер: Инесса Трегубова, Geodata analyst at Яндекс Лавка В докладе расскажу, как и зачем используют геоданные в маркетинге. Обсудим специфику сбора и процессинга пространственных данных, а также несколько ML моделей, с помощью которых можно делать выводы о том, насколько тот или иной участок подходит для целей бизнеса. Покажем, какие задачи можно решать в бизнесе. 00:00 начало видео

01:09 задачи гео-маркетинга

03:18 типы данных

04:05 меры сходства между объектами

07:31 системы агреггации данных в пространстве

10:14 проекции представления данных (crs)

11:11 форматы датасетов

12:57 пр…

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021

Телеграм-канал https://t.me/mlinmarketing Спикер: Данил Гиздатуллин, Data Scientist В рекомендательных системах есть понятия пользователя и айтема. Типичная задача рекомендаций - рекомендации айтемов пользователям, но также одной из важных задач является задача рекомендации айтемов к айтемам. В докладе рассмотрим несколько вариантов её решения: от использования коллаборативной фильтрации до модели ранжирования. Презентация доклада: https://drive.google.com/file/d/1F0mmU0W4mbOPKRpfYr0JaP6I82L7PS8z/view?usp=sharing Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

ODS Summer of Code 2021 | Intel & SberCloud track https://ods.ai/tracks/cloudcity2021 Спикер: Андрей Ануфриев, Старший инженер-исследователь в области глубокого обучения, Intel Вступить в сообщество: https://ods.ai/events/datafest2021/join Соцсети Data Fest & ODS Summer of Code:

https://t.me/datafest

https://vk.com/datafest

ODS Course Fest #1 https://datafest.ru/course/ Зарегистрироваться и получить доступ ко всем курсам и мероприятиям сезона: https://ods.ai/events/course_fest_1 Вступить в сообщество: https://ods.ai/ Соцсети Data Fest & Course Fest: https://t.me/datafest

https://vk.com/datafest

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021

Телеграм-канал https://t.me/mlinmarketing Спикер: Антон Киселев, Marketing Analytics Producer at Playrix

* 10 лет в data (ритейл, игры, телеком)

* 15 DS проектов в проде

* 40 DS специалистов принял на работу

* 3 DS отдела построил с нуля Антон расскажет, как оптимизировали и автоматизировали ценообразование моторного топлива в одной российской топливной компании. Как учились менять цены на лету, как Марков, RL и многорукие бандиты помогли вывести argmax в продакшн и что из всего этого вышло. 00:00 introduction

01:15 динамическое ценообразование в бизнесе

04:00 цели и задачи использования

05:25 ценообразование в топливном рит…

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021

Телеграм-канал https://t.me/mlinmarketing Спикер: Артем Агафонов, Data Scientist at Mail Ru Group Выбор удачной геопозиции является одной из важных стратегических задач для бизнеса, будь это строительная компания или компания розничной торговли (магазины, рестораны, банки). Популярность локации, доступность с точки зрения пешеходного или транспортного потока, наличие конкурентов в геоточке определяют важнейшие характеристики успешности бизнеса. В докладе рассмотрены примеры того, как большие данные помогают автоматизировать процесс отбора географического положения для нового объекта. Презентация доклада: https://drive.google…

Try NordVPN free for 30 days at https://nordvpn.com/Primer More than you ever wanted to know about Hamilton's rule:

https://users.ox.ac.uk/~grafen/cv/oseb.pdf For discussion and updates

- Twitter: @primerlearning

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning Plush blobs and other merch: https://store.dftba.com/collections/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning Made with Unity and Manim

https://github.com/Helpsypoo/PrimerUnity

https://www.manim.community Music by Mathieu Keith. For business inquiries: mathieu.keith@gmail.com Several other inputs into the graphics are from public domain contributions to blendswap.com Made possible by …

Brilliant: http://www.brilliant.org/primer Papers:

- https://www.researchgate.net/publication/41910312_Altruism_Spite_and_Greenbeards

- https://www.reed.edu/biology/professors/srenn/pages/teaching/2007_syllabus/2007_readings/a13_Keller_1998.pdf For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

- Twitter: @primerlearning Sometimes streaming myself working on these monstrosities:

- Twitch: https://www.twitch.tv/primerjustin Made with Unity

https://github.com/Helpsypoo/PrimerUnity Music by Mathieu Keith. For business inquiries: mathieu.keith@gmail.com Several other inputs into the graphics are from public domain contributions to blendswap.com Plush blo…

Kevin Systrom is the co-founder and former CEO of Instagram.

Please support this podcast by checking out our sponsors:– Theragun: https://therabody.com/lex to get 30 day trial– NI: https://www.ni.com/perspectives– GiveWell: https://www.givewell.org/ and use code LEX to get donation matched up to $1k– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Fundrise: https://fundrise.com/lexEPISODE LINKS:Kevin’s Instagram: https://www.instagram.com/kevin/Kevin’s Twitter: https://twitter.com/kevinKevin’s LinkedIn: https://www.linkedin.com/in/kevinsystromPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti…

Ben Askren is a wrestler and MMA fighter, former Bellator and ONE Championship welterweight champion, a two-time NCAA wrestling champion and four-time finalist.

Please support this podcast by checking out our sponsors:– Notion: https://notion.com/startups to get up to $1000 off team plan– NI: https://www.ni.com/perspectives– Onnit: https://lexfridman.com/onnit to get up to 10% off– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– NetSuite: http://netsuite.com/lex to get free product tourEPISODE LINKS:Ben’s Twitter: https://twitter.com/BenaskrenBen’s Instagram: https://www.instagram.com/benaskren/Ben’s Website: https://www.benaskren.com/Ben’s Wrestling Aca…

Boris Sofman is the Senior Director Of Engineering and Head of Trucking at Waymo, formerly the Google Self-Driving Car project.

He was also the CEO and co-founder of Anki, a home robotics company.

Please support this podcast by checking out our sponsors:– LMNT: https://drinkLMNT.com/lex to get free sample pack– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– ROKA: https://roka.com/ and use code LEX to get 20% off your first order– Indeed: https://indeed.com/lex to get $75 credit– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Boris’s Twitter: https://twitter.com/bsofmanBoris’s LinkedIn: https://www.linkedin.com/in/bsofmanWaymo…

Neal Stephenson is a sci-fi writer (Snow Crash, Cryptonomicon, and new book Termination Shock), former Chief Futurist at Magic Leap and first employee of Blue Origin.

Please support this podcast by checking out our sponsors:– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– InsideTracker: https://insidetracker.com/lex and use code Lex25 to get 25% off– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Grammarly: https://grammarly.com/lex to get 20% off premium– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months freeEPISODE LINKS:Neal’s Twitter: https://twitter.com/nealstephensonNeal’s Website: https…

Niall Ferguson is a historian at Hoover Institution, Stanford University.

He is the author of 16 books on the history of money, war, power, and catastrophe.

Please support this podcast by checking out our sponsors:– The Prisoner Wine Company: https://theprisonerwine.com/lex to get 20% off & free shipping– Stripe: https://stripe.com– Coinbase: https://coinbase.com/lex to get $5 in free Bitcoin– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:Niall’s Twitter: https://twitter.com/nfergusNiall’s Website: https://www.niallferguson.comUniversity of Austin: https://uaustin.org/Doom: The Politics of …

Francis Collins is the director of the NIH and former head of the Human Genome Project.

Please support this podcast by checking out our sponsors:– Ten Thousand: https://www.tenthousand.cc/ and use code LEX to get 15% off– Allform: https://allform.com/lex to get 20% off– SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security camera– NI: https://www.ni.com/perspectives– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 offEPISODE LINKS:Francis’s Twitter: https://twitter.com/NIHDirectorNIH: https://www.nih.govHuman Genome Project: https://www.genome.gov/human-genome-projectPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https…

Steve Viscelli is a former truck driver and now an economic sociologist at University of Pennsylvania studying freight transportation, including autonomous trucks.

Please support this podcast by checking out our sponsors:– Shopify: https://shopify.com/lex to get 14-day free trial– ROKA: https://roka.com/ and use code LEX to get 20% off your first order– Sunbasket: https://sunbasket.com/lex and use code LEX to get $35 off– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Steve’s Website: https://www.steveviscelli.com/Big Rig (book): https://amzn.to/3EbaofPWill Robotic Trucks Be “Sweatshops on Wheels…

Jimmy Pedro is a judo competitor and coach, world champion, 3x world medalist, 2x Olympic medalist.

Please support this podcast by checking out our sponsors:– ROKA: https://roka.com/ and use code LEX to get 20% off your first order– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Ladder: https://ladderlife.com/lex– Linode: https://linode.com/lex to get $100 free credit– MasterClass: https://masterclass.com/lex to get 15% offEPISODE LINKS:Jimmy’s Instagram: https://www.instagram.com/jimmypedrousaJimmy’s Links: https://linktr.ee/jimmypedroJimmy’s Twitter: https://twitter.com/jimmypedrousaJimmy’s Website: https://www.jimmypedro.com/American Judo Sys…

Michael Mina is an immunologist, epidemiologist, and physician at Harvard.

Please support this podcast by checking out our sponsors:– Notion: https://notion.com/startups to get up to $1000 off team plan– Quip: https://getquip.com/lex to get first refill free– Theragun: https://therabody.com/lex to get 30 day trial– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% offEPISODE LINKS:Michael’s Twitter: https://twitter.com/michaelmina_labPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS:…

Stephen Wolfram is a computer scientist, mathematician, and theoretical physicist.

Please support this podcast by checking out our sponsors:– ROKA: https://roka.com/ and use code LEX to get 20% off your first order– FightCamp: https://joinfightcamp.com/lex to get free shipping– Onnit: https://lexfridman.com/onnit to get up to 10% off– Indeed: https://indeed.com/lex to get $75 credit– Fundrise: https://fundrise.com/lexEPISODE LINKS:Stephen’s Twitter: https://twitter.com/stephen_wolframStephen’s Blog: https://writings.stephenwolfram.comWolfram Physics Project: https://www.wolframphysics.orgA New Kind of Science (book): https://amzn.to/30XoEunFundamental Theory of Physics (book): https://amzn.…

Carl Hart is a psychologist at Columbia University.

Please support this podcast by checking out our sponsors:– InsideTracker: https://insidetracker.com/lex and use code Lex25 to get 25% off– Ten Thousand: https://www.tenthousand.cc/ and use code LEX to get 15% off– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months freeEPISODE LINKS:Carl’s Twitter: https://twitter.com/drcarlhartCarl’s Website: https://drcarlhart.comDrug Use for Grown-Ups (book): https://amzn.to/3lVpq2YPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: http…

Brian Greene is a theoretical physicist.

Please support this podcast by checking out our sponsors:– The Prisoner Wine Company: https://theprisonerwine.com/lex to get 20% off & free shipping– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– LMNT: https://drinkLMNT.com/lex to get free sample pack– BetterHelp: https://betterhelp.com/lex to get 10% off– NI: https://www.ni.com/perspectivesEPISODE LINKS:Brian’s Twitter: https://twitter.com/bgreeneBrian’s Website: http://www.briangreene.org/Until the End of Time (book): https://amzn.to/2XuqXUiPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEw…

Alex Gladstein is the Chief Strategy Officer at the Human Rights Foundation and the Oslo Freedom Forum.

Please support this podcast by checking out our sponsors:– Stripe: https://stripe.com– Paperspace: https://gradient.run/lex to get $15 credit– Codecademy: https://codecademy.com and use code LEX to get 15% off– NI: https://www.ni.com/perspectives– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savingsEPISODE LINKS:Alex’s Twitter: https://twitter.com/gladsteinThe Little Bitcoin Book: https://littlebitcoinbook.com/Human Rights Foundation: https://hrf.org/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: …

Kelsi Sheren is a veteran, artillery gunner, and founder of Brass and Unity.

Please support this podcast by checking out our sponsors:– Shopify: https://shopify.com/lex to get 14-day free trial– Justworks: https://justworks.com– Novo: https://banknovo.com/lex– Indeed: https://indeed.com/lex to get $75 credit– Onnit: https://lexfridman.com/onnit to get up to 10% offEPISODE LINKS:Kelsi’s Twitter: https://twitter.com/kelsiburnsKelsi’s Website: https://brassandunity.comKelsi’s Instagram: https://www.instagram.com/kelsie_sherenCharity: https://www.heroicheartsproject.org/Do the F*cking Work (book): https://amzn.to/3mGhguFNotes from Underground (book): https://amzn.to/3mN5TRFBrothers in Arms (son…

For Microsoft researchers, COVID-19 was a call to action.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

In this episode of The New Future of Work series, Chief Scientist Jaime Teevan and Senior Principal Researcher Siddharth Suri explore the many ways people were impacted by work shifts during the COVID-19 pandemic.

The research that Siddharth Suri describes in this podcast was jointly done with Hana Wolf of Li…

For Microsoft researchers, COVID-19 was a call to action.

The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

They also talk about what an “anatomy of hybrid work” might look like and som…

For Microsoft researchers, COVID-19 was a call to action.

The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

They also explore why remote work came with a spike in phishing threats, what…

Unlocking the challenge of molecular simulation has the potential to yield significant breakthroughs in how we tackle such societal issues as climate change, drug discovery, and the treatment of disease, and Microsoft is ramping up its efforts in the space.

In this episode, Chris Bishop, Lab Director of Microsoft Research Cambridge, welcomes renowned machine learning researcher Max Welling to the Microsoft Research team as head of the new Amsterdam lab.

Connecting over their shared physics background and vision for molecular simulation, Bishop and Welling explore several fascinating topics, including a future in which machine learning and quantum computing will be used in tandem to model mo…

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

In this episode of The New Future of Work series, Chief Scientist Jaime Teevan and Principal Productivity Engineer Brian Houck discuss what the massive shift to remote work meant for developers—both employees of Microsoft and customers using Microsoft developer platforms to support their work.

They’ll talk about how taking a holistic approach to developer productivi…

For Microsoft researchers, COVID-19 was a call to action.

The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

They also explore how people already working from home helped them better und…

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

In this episode of The New Future of Work series of the podcast, Chief Scientist Jaime Teevan and Abigail Sellen, Deputy Lab Director at Microsoft Research Cambridge in the United Kingdom, explore the dynamics of meetings and collaborations in the context of remote work.

They specifically address the difference between weak and strong ties in our professional networ…

For Microsoft researchers, COVID-19 was a call to action.

The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

They’ll discuss the role of research during times of disruption, the widening…

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society.

In this episode, Senior Principal Researcher Hunt Allcott talks with Postdoctoral Researcher Evan Rose about Allcott’s work exploring the everyday decisions people face, like buying fuel-efficient cars or taking out payday loans, and how a clearer understanding of these decisions can shape meaningful public policy.

Allcott shares how his and others’ research shows that policy can often have compl…

Interviewed by Senior Principal Researcher Hunt Allcott, Economist David Rothschild discusses how the news media has evolved alongside social media and the internet, from story development to distribution of news via aggregators and wire services.

Rothschild illuminates how and where people are consuming news and shares some of the strategies he’s seeing news outlets use to appeal to their audiences.

He also covers research insights into media bias, misinformation, and how this knowledge could inform the future of news for the better.

In addition, the researchers talk about Rothschild’s work with Project Ratio, which looks at how the news ecosystem impacts public opinion and political polar…

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society.

In this episode, Senior Principal Researcher Dr. Hunt Allcott speaks with Microsoft Research New England office mate and Senior Principal Researcher Dr. Greg Lewis.

Together, they cover the connection between causal machine learning and economics research, the motivations of buyers and sellers on e-commerce platforms, and how ad targeting and data practices could evolve to foster a more symbiotic…

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society.

In this episode, Dr. Hunt Allcott, Senior Principal Researcher at Microsoft Research New England, talks with Dr. Evan Rose, Postdoctoral Researcher, whom Allcott describes as “one of the most engaging and talented researchers in applied microeconomics today.” They’ll discuss how Rose’s experience teaching adult learners at San Quentin State Prison has resonated throughout his research, and they’l…

Today, people around the globe—from teachers to small-business owners to finance executives—use Microsoft Excel to make sense of the information that occupies their respective worlds, and whether they realize it or not, in doing so, they’re taking on the role of programmer.

In this episode, Senior Principal Research Manager Andy Gordon, who leads the Calc Intelligence team at Microsoft Research, and Senior Principal Researcher Simon Peyton Jones provide an inside account of the journey Excel has taken as a programming language, including the expansion of data types that has unlocked greater functionality and the release of the LAMBDA function, which makes the Excel formula language Turing-c…

This episode is the second in our current series on PhD applications.

How do PhD programs in Europe differ from PhD programs in the US, and how should people decide between them?

In this episode, we invite Barbara Plank…

This episode is the first in our current series on PhD applications.

How should people prepare their applications to PhD programs in NLP?

In this episode, we invite Nathan Schneider (Professor of Linguistics and Compute…

In this episode, we discussed the Alexa Prize Socialbot Grand Challenge and this year's winning submission, Alquist 4.0, with Petr Marek, a member of the winning team.

Petr gave us an overview of their submission, the de…

What can NLP researchers learn from Human Computer Interaction (HCI) research?

We chatted with Nanna Inie and Leon Derczynski to find out.

We discussed HCI's research processes including methods of inquiry, the data anno…

In this episode, we talk with Lisa Beinborn, an assistant professor at Vrije Universiteit Amsterdam, about how to use human cognitive signals to improve and analyze NLP models.

We start by discussing different kinds of c…

In this episode, we talk to Shunyu Yao about recent insights into how transformers can represent hierarchical structure in language.

Bounded-depth hierarchical structure is thought to be a key feature of natural language…

We discussed adversarial dataset construction and dynamic benchmarking in this episode with Douwe Kiela, a research scientist at Facebook AI Research who has been working on a dynamic benchmarking platform called Dynaben…

We invited members of Masakhane, Tosin Adewumi and Perez Ogayo, to talk about their EMNLP Findings paper that discusses why typical research is limited for low-resourced NLP and how participatory research can help.

We invited Lisa Li to talk about her recent work, Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Prefix tuning is a lightweight alternative to finetuning, and the idea is to tune only a fixed-length task-sp…

How can we build Visual Question Answering systems for real users?

For this episode, we chatted with Danna Gurari, about her work in building datasets and models towards VQA for people who are blind.

We talked about the …

We use cookies for various purposes including analytics and personalized marketing.

By continuing to use the service, you agree to our use of cookies as described in the Cookie Policy

We use cookies for various purposes including analytics and personalized marketing.

By continuing to use the service, you agree to our use of cookies as described in the Cookie Policy

The retail holiday “black Friday” occurs the day after Thanksgiving in the United States. It’s dubbed this because many retail companies spend the first 10 months of the year running at a loss (in the red) before finally earning as much as 80% of their revenue in the last two months of the year. This episode features four interviews with guests bringing unique data-driven perspectives on the topic of analyzing this seeming outlier in a time series dataset.

Alex Terenin, Postdoctoral Research Associate at the University of Cambridge, joins us today to talk about his work "Aligning Time Series on Incomparable Spaces."

Today we are joined again by Ben Fulcher, leader of the Dynamics and Neural Systems Group at the University of Sydney in Australia, to talk about hctsa, a software package for running highly comparative time-series analysis.

Gerrit van den Burg, Postdoctoral Researcher at The Alan Turing Institute, joins us today to discuss his work "An Evaluation of Change Point Detection Algorithms."

Bahman Rostami-Tabar, Senior Lecturer in Management Science at Cardiff University, joins us today to talk about his work "Forecasting and its Beneficiaries."

Alex Mallen, Computer Science student at the University of Washington, and Henning Lange, a Postdoctoral Scholar in Applied Math at the University of Washington, join us today to share their work "Deep Probabilistic Koopman: Long-term Time-Series Forecasting Under Periodic Uncertainties."

Fotios Petropoulos, Professor of Management Science at the University of Bath in The U.K., joins us today to talk about his work "Fast and Frugal Tim Series Forecasting." WORKS MENTIONED Fast and Frugal Time Series Forecasting by Fotios Petropoulos, Yael Grushka-Cockayne SOCIAL MEDIA Twitter

Manie Tadayon, a PhD graduate from the ECE department at University of California, Los Angeles, joins us today to talk about his work “Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach.”

Sankeerth Rao Karingula, ML Researcher at Palo Alto Networks, joins us today to talk about his work “Boosted Embeddings for Time Series Forecasting.” Works Mentioned Boosted Embeddings for Time Series Forecasting by Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi, Mehrnaz Amjadi, Deokwoo Jung, Ricky Si, Charanraj Thimmisetty, Luisa Polania Cabrera, Marjorie Sayer, Claudionor Nunes Coelho Jr Social Media Linkedin Twitter LOD Conference 2021

Samya Tajmouati, a PhD student in Data Science at the University of Science of Kenitra, Morocco, joins us today to discuss her work Applying K-Nearest Neighbors to Time Series Forecasting: Two New Approaches.

Dr. Feng Li, (@f3ngli) is an Associate Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. He joins us today to discuss his work Distributed ARIMA Models for Ultra-long Time Series.

Angus Dempster, PhD Student at Monash University in Australia, comes on today to talk about MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification, a fast deterministic transform for time series classification. MINIROCKET reformulates ROCKET, gaining a 75x improvement on larger datasets with essentially the same performance. In this episode, we talk about the insights that realized this speedup as well as use cases.

David Daly, Performance Engineer at MongoDB, joins us today to discuss "The Use of Change Point Detection to Identify Software Performance Regressions in a Continuous Integration System".

Chongshou Li, Associate Professor at Southwest Jiaotong University in China, joins us today to talk about his work Why are the ARIMA and SARIMA not Sufficient.

Ben Fulcher, Senior Lecturer at the School of Physics at the University of Sydney in Australia, comes on today to talk about his project Comp Engine.

I finish up our three-part series on the results of the O’Reilly Survey, looking at the highest-paying data frameworks.

Additional materials: www.superdatascience.com/526

Karen Jean-Francois joins us to discuss how she wants to empower her team members and a wider audience of data scientists battling imposter syndrome.

In this episode you will learn:• Karen’s background as a hurdler [4:…

In this episode, I go over the highest-paying data tools based on the O’Reilly survey.

Additional materials: www.superdatascience.com/524

Wes McKinney joins us to discuss the history and philosophy of pandas and Apache Arrow as well as his continued work in open source tools.

In this episode you will learn:• History of pandas [7:29]• The trends of R and…

I provide you with some quick definitions of data tools vs data platforms to prep us for deep dives in future episodes.

Additional materials: www.superdatascience.com/522

Khuyen Tran joins us to discuss her work as a prolific technical writer and undergraduate data science student.

In this episode you will learn:• Khuyen’s online writing [4:00]• Book writing [8:50]• How you can increa…

I take a look at the results of O’Reilly’s survey on salaries for data scientists in 2021.

Additional materials: www.superdatascience.com/520

James Hodson joins us to discuss his philosophy and work at A.I.

For Good and how they aim to promote sustainability and A.I.

use for social issues.

In this episode you will learn:• AI for Good [5:17]• Founding of AI …

This week, I provide a short but important bit of advice on failure.

Additional materials: www.superdatascience.com/518

Sadie St. Lawrence talks in-depth about her extensive work as a data science educator through both online and collegiate courses as well as her organization for diversifying data science careers.

In this episode you wil…

In this episode, I finish up my saga into the effects of caffeine on productivity.

Additional materials: www.superdatascience.com/516

Chrys Wu joins us to discuss her community organizations, her tips, and her recommended resources for building data science communities for impact.

In this episode you will learn:• The world of K-Pop [ 4:07]• Chrys’s …

In this episode, I dive into the nuts and bolts of data on my experiment into caffeine and productivity.

Additional materials: www.superdatascience.com/514

Denis Rothman joins us to discuss his writing work in natural language processing, explainable AI, and more!

In this episode you will learn:• What are transformers and their applications?

[7:54]• Denis’s book on expla…

I dive into a personal experiment to test my productivity relative to my coffee intake and if caffeine is actually hurting my productivity.

Additional materials: www.superdatascience.com/512

November 16, 2021 podcastThis episode summarizes a study about trends of AI in 2021, the way AI is perceived by people of different background and some other weird questions.

For instance, would you have sexual intercourse with a robot?

Would you be in a relationship with an artificial intelligence?

The study has been conducted by Tidio and reported at https://www.tidio.com/blog/ai-trends/SponsorsThis episode is supported by Amethix Technologies.

Amethix uses machine learning and advanced analytics to empower people and organizations to ask and answer complex questions like never before.

November 9, 2021 podcastIf you think deep learning is a method to get to AGI, think again.

Humans, as well as all mammals think in a… composable way.

SponsorsThis episode is brought to you by Advanced RISC Machines (ARM).

ARM is a family of reduced instruction set computing architectures for computer processors https://www.arm.com/Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

November 2, 2021 podcastAI, ethics, and explainability.

We answer such questions in this amazing episode with Erika Agostinelli from the AI Elite team at IBM.

Sponsored by Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

ReferencesAIX360:https://aix360.mybluemix.net/Questioning the AI: Informing Design Practices for Explainable AI User Experienceshttps://arxiv.org/abs/2001.…

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits.

By clicking “Accept”, you consent to the use of ALL the cookies.

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits.

By clicking “Accept”, you consent to the use of ALL the cookies.

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits.

By clicking “Accept”, you consent to the use of ALL the cookies.

October 21, 2021 podcastOur SponsorsQuantum MetricStay off the naughty list this holiday season by reducing customer friction, increasing conversions, and personalizing the shopping experience.

Visit us at quantummetric.com/podoffer and see if you qualify to receive our “12 Days of Insights” offer with code DATASCIENCE.

This offer gives you 12-day access to our platform coupled with a bespoke insight report that will help you identify where customers are struggling or engaging in your digital product.

Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logist…

October 21, 2021 podcastA few weeks ago I was the guest of a very interesting show called “AI Today”.

In that episode I talked about some of the biggest trends emerging in AI and machine learning today as well as how organizations are dealing with and managing their data.

Visit us at quantummetric.com/podoffer and see if you qualify to receive our “12 Days of Insights” offer with code DATASCIENCE.

Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermed…

Generative Adversarial Networks for synthetic data generation?

There is a new method called Generative Teaching Networks, that uses similar concepts – just quite the opposite 😛 – to train models faster, better and with less data.

Visit us at quantummetric.com/podoffer and see if you qualify to receive our “12 Days of Insights” offer with code DATASCIENCE.

Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models th…

October 21, 2021 podcastIt’s time we get serious about replacing the CSV format with something that, guess what?

In this episode I explain the good parts of CSV files and the not so good ones.

Visit us at quantummetric.com/podoffer and see if you qualify to receive our “12 Days of Insights” offer with code DATASCIENCE.

Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

August 18, 2021 podcastIs reinforcement learning sufficient to build truly intelligent machines?

Our SponsorsQuantum MetricStay off the naughty list this holiday season by reducing customer friction, increasing conversions, and personalizing the shopping experience.

Visit us at quantummetric.com/podoffer and see if you qualify to receive our “12 Days of Insights” offer with code DATASCIENCE.

Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation…

August 18, 2021 podcastIn this episode I have a wonderful chat with Ronald Schmelzer and Kathleen Walch, authors of “AI Today” the top podcast for those wanting a no-hype, practical, real-world insight into what enterprises, public sector agencies, thought leaders, leading technology companies, pundits, and experts are doing with AI today.

Sponsored byQuantum Metric Did you know that 2021 holiday ecommerce sales are expected to exceed 2020 benchmarks?Are you prepared to capture every customer revenue opportunity?

Visit their website at quantummetric.com/podoffer and see if you qualify to receive their “12 Days of Insights” offer with code DATASCIENCE.

Sponsored by Amethix TechnologiesAmethi…

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits.

By clicking “Accept”, you consent to the use of ALL the cookies.

Remember the Netflix challenge? It was a ton of money for the one who would have cracked the problem of recommending the best possible movie. Was it a fair challenge? […]

The post The Netflix challenge. Fair or what? (Ep. 162) appeared first on Podcast Data science at home.

In this episode Fetch AI CTO Jonathan Ward speaks about decentralization, AI, blockchain for smart cities and the enterprise.Below some great links about collective learning, smart contracts in Rust and […]

The post Artificial Intelligence for Blockchains with Jonathan Ward CTO of Fetch AI (Ep. 161) appeared first on Podcast Data science at home.