Yannic Kilcher Videos (Audio Only)

#openai #math #imo This is an interview with Stanislas Polu, research engineer at OpenAI and first author of the paper "Formal Mathematics Statement Curriculum Learning". Watch the paper review here: https://youtu.be/lvYVuOmUVs8 OUTLINE: 0:00 - Intro 2:00 - How do you explain the big public reaction? 4:00 - What's the history behind the paper? 6:15 - How does algorithmic formal math work? 13:10 - How does expert iteration replace self-play? 22:30 - How is the language model trained and used? 30:50 - Why is every model fine-tuned on the initial state? 33:05 - What if we want to prove something we don't know already? 40:35 - How can machines and humans work together? 43:40 - Aren't most produced statements useless? 46:20 - A deeper look at the experimental results 50:10 - What were the high and low points during the research? 54:25 - Where do we go from here? Paper: https://arxiv.org/abs/2202.01344 miniF2F benchmark: https://github.com/openai/miniF2F Follow Stan here: https://twitter.com/spolu Abstract: We explore the use of expert iteration in the context of language modeling applied to formal mathematics. We show that at same compute budget, expert iteration, by which we mean proof search interleaved with learning, dramatically outperforms proof search only. We also observe that when applied to a collection of formal statements of sufficiently varied difficulty, expert iteration is capable of finding and solving a curriculum of increasingly difficult problems, without the need for associated ground-truth proofs. Finally, by applying this expert iteration to a manually curated set of problem statements, we achieve state-of-the-art on the miniF2F benchmark, automatically solving multiple challenging problems drawn from high school olympiads. Authors: Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever 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/yann... 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 financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#openai #math #imo Formal mathematics is a challenging area for both humans and machines. For humans, formal proofs require very tedious and meticulous specifications of every last detail and results in very long, overly cumbersome and verbose outputs. For machines, the discreteness and sparse reward nature of the problem presents a significant problem, which is classically tackled by brute force search, guided by a couple of heuristics. Previously, language models have been employed to better guide these proof searches and delivered significant improvements, but automated systems are still far from usable. This paper introduces another concept: An expert iteration procedure is employed to iteratively produce more and more challenging, but solvable problems for the machine to train on, which results in an automated curriculum, and a final algorithm that performs well above the previous models. OpenAI used this method to even solve two problems of the international math olympiad, which was previously infeasible for AI systems. OUTLINE: 0:00 - Intro 2:35 - Paper Overview 5:50 - How do formal proofs work? 9:35 - How expert iteration creates a curriculum 16:50 - Model, data, and training procedure 25:30 - Predicting proof lengths for guiding search 29:10 - Bootstrapping expert iteration 34:10 - Experimental evaluation & scaling properties 40:10 - Results on synthetic data 44:15 - Solving real math problems 47:15 - Discussion & comments Paper: https://arxiv.org/abs/2202.01344 miniF2F benchmark: https://github.com/openai/miniF2F Abstract: We explore the use of expert iteration in the context of language modeling applied to formal mathematics. We show that at same compute budget, expert iteration, by which we mean proof search interleaved with learning, dramatically outperforms proof search only. We also observe that when applied to a collection of formal statements of sufficiently varied difficulty, expert iteration is capable of finding and solving a curriculum of increasingly difficult problems, without the need for associated ground-truth proofs. Finally, by applying this expert iteration to a manually curated set of problem statements, we achieve state-of-the-art on the miniF2F benchmark, automatically solving multiple challenging problems drawn from high school olympiads. Authors: Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, Ilya Sutskever 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/yann... 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 financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Updates on what's going on in the ML world! Check out w&b's alerts feature: https://wandb.me/yannic OUTLINE: 0:00 - Intro 0:20 - Sponsor: Weights & Biases 2:35 - DeepMind uses Reinforcement Learning to control nuclear fusion 4:35 - Google responds to carbon emission estimates 8:40 - Yann LeCun proposes new architecture for world models 11:05 - Fruit fly neurons may perform multiplication 12:00 - Emojisearch App 12:30 - Ar5iv officially in arXiv labs 12:55 - Language Model Consciousness & Media Hype 16:45 - Vision models are more fair when trained on uncurated data 18:30 - CLIPasso 19:15 - NLP with Transformers Book 20:15 - Helpful Things 26:00 - US Office: AI can't copyright its art Sponsor: Weights & Biases https://wandb.me/yannic References: https://wandb.me/yannic DeepMind uses RL to control nuclear fusion https://deepmind.com/blog/article/Acc... https://www.nature.com/articles/s4158... https://www.nature.com/articles/s4158... https://www.alexirpan.com/2018/02/14/... Google responds to carbon emission estimates https://ai.googleblog.com/2022/02/goo... Yann LeCun proposes new architecture for world models https://ai.facebook.com/blog/yann-lec... Fruit fly neurons may perform multiplication https://www.nature.com/articles/s4158... Emojisearch App https://twitter.com/lilianweng/status... https://www.emojisearch.app/ https://github.com/lilianweng/emoji-s... Ar5iv officially in arXiv labs https://blog.arxiv.org/2022/02/21/arx... Tech media may be only slightly conscious https://twitter.com/ilyasut/status/14... https://futurism.com/the-byte/openai-... https://interestingengineering.com/ai... https://futurism.com/mit-researcher-c... https://www.dailymail.co.uk/sciencete... https://futurism.com/conscious-ai-bac... https://www.dailystar.co.uk/tech/news... Vision models are more fair when trained on uncurated data https://arxiv.org/pdf/2202.08360.pdf CLIPasso https://clipasso.github.io/clipasso/ NLP with Transformers Book https://www.amazon.de/dp/1098103246?l... Helpful Things https://github.com/j3soon/tbparse https://github.com/openvinotoolkit/an... https://liuliu66.github.io/articulati... https://github.com/RobertTLange/evosax https://github.com/google/evojax https://github.com/google/evojax/pull/9 https://github.com/facebookresearch/t... https://standard-ai.github.io/Standar... https://twitter.com/PatrickPlaten/sta... https://aimagelab.ing.unimore.it/imag... https://github.com/yashbhalgat/HashNe... https://github.com/patrick-kidger/dif... https://github.com/AI4Finance-Foundat... https://huggingface.co/AI-Nordics/ber... https://huggingface.co/AI-Nordics/gpt... https://paperswithcode.com/dataset/muld https://github.com/JonasGeiping/breac... https://github.com/Weixin-Liang/MetaS... US Office: AI can't copyright its art https://www.theverge.com/2022/2/21/22... https://www.urbasm.com/2016/05/artifi... 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/yann... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191

An interview with the creators of AlphaCode! Paper review video here: https://youtu.be/s9UAOmyah1A OUTLINE: 0:00 - Intro 1:10 - Media Reception 5:10 - How did the project go from start to finish? 9:15 - Does the model understand its own code? 14:45 - Are there plans to reduce the number of samples? 16:15 - Could one do smarter filtering of samples? 18:55 - How crucial are the public test cases? 21:55 - Could we imagine an adversarial method? 24:45 - How are coding problems even made? 27:40 - Does AlphaCode evaluate a solution's asymptotic complexity? 33:15 - Are our sampling procedures inappropriate for diversity? 36:30 - Are all generated solutions as instructive as the example? 41:30 - How are synthetic examples created during training? 42:30 - What were high and low points during this research? 45:25 - What was the most valid criticism after publication? 47:40 - What are applications in the real world? 51:00 - Where do we go from here? Paper: https://storage.googleapis.com/deepmi... Code: https://github.com/deepmind/code_cont... Abstract: Programming is a powerful and ubiquitous problem-solving tool. Developing systems that can assist programmers or even generate programs independently could make programming more productive and accessible, yet so far incorporating innovations in AI has proven challenging. Recent large-scale language models have demonstrated an impressive ability to generate code, and are now able to complete simple programming tasks. However, these models still perform poorly when evaluated on more complex, unseen problems that require problem-solving skills beyond simply translating instructions into code. For example, competitive programming problems which require an understanding of algorithms and complex natural language remain extremely challenging. To address this gap, we introduce AlphaCode, a system for code generation that can create novel solutions to these problems that require deeper reasoning. Evaluated on recent programming competitions on the Codeforces platform, AlphaCode achieved on average a ranking of top 54.3% in programming competitions with more than 5,000 participants. We found that three key components were critical to achieve good and reliable performance: (1) an extensive and clean competitive programming dataset for training and evaluation, (2) large and efficient-to-sample transformer-based architectures, and (3) large-scale model sampling to explore the search space, followed by filtering based on program behavior to a small set of submissions. Authors: Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu and Oriol Vinyals Links: Merch: store.ykilcher.com 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/yann... 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 financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher

#ai #alphacode #deepmind AlphaCode is an automated system that can solve competitive programing exercises. The authors found an interesting combination of language models, large-scale sampling, and clever techniques to filter and subsequently cluster the resulting programs, which lets the system perform on the level of an average competitor in real competitions. In this video, we take a deep dive into AlphaCode's design, architecture, and experimental evaluation. The paper is very well structured and the empirical results are super interesting! OUTLINE: 0:00 - Intro 2:10 - Paper Overview 3:30 - An example problem from competitive programming 8:00 - AlphaCode system overview 14:00 - Filtering out wrong solutions 17:15 - Clustering equivalent generated programs 21:50 - Model configurations & engineering choices 24:30 - Adding privileged information to the input & more tricks 28:15 - Experimental Results (very interesting!) Paper: https://storage.googleapis.com/deepmi... Code: https://github.com/deepmind/code_cont... Abstract: Programming is a powerful and ubiquitous problem-solving tool. Developing systems that can assist programmers or even generate programs independently could make programming more productive and accessible, yet so far incorporating innovations in AI has proven challenging. Recent large-scale language models have demonstrated an impressive ability to generate code, and are now able to complete simple programming tasks. However, these models still perform poorly when evaluated on more complex, unseen problems that require problem-solving skills beyond simply translating instructions into code. For example, competitive programming problems which require an understanding of algorithms and complex natural language remain extremely challenging. To address this gap, we introduce AlphaCode, a system for code generation that can create novel solutions to these problems that require deeper reasoning. Evaluated on recent programming competitions on the Codeforces platform, AlphaCode achieved on average a ranking of top 54.3% in programming competitions with more than 5,000 participants. We found that three key components were critical to achieve good and reliable performance: (1) an extensive and clean competitive programming dataset for training and evaluation, (2) large and efficient-to-sample transformer-based architectures, and (3) large-scale model sampling to explore the search space, followed by filtering based on program behavior to a small set of submissions. Authors: Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu and Oriol Vinyals Links: Merch: store.ykilcher.com 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/yann... 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 financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq

#wikipedia #reinforcementlearning #languagemodels Original paper review here: https://youtu.be/XHGh19Hbx48 Machel Reid and Yutaro Yamada join me to discuss their recent paper on langauge model pre-training for decision transformers in offline reinforcement learning. OUTLINE: 0:00 - Intro 1:00 - Brief paper, setup & idea recap 7:30 - Main experimental results & high standard deviations 10:00 - Why is there no clear winner? 13:00 - Why are bigger models not a lot better? 14:30 - What’s behind the name ChibiT? 15:30 - Why is iGPT underperforming? 19:15 - How are tokens distributed in Reinforcement Learning? 22:00 - What other domains could have good properties to transfer? 24:20 - A deeper dive into the models' attention patterns 33:30 - Codebase, model sizes, and compute requirements 37:30 - Scaling behavior of pre-trained models 40:05 - What did not work out in this project? 42:00 - How can people get started and where to go next? Paper: https://arxiv.org/abs/2201.12122 Code: https://github.com/machelreid/can-wik... My Video on Decision Transformer: https://youtu.be/-buULmf7dec Abstract: Fine-tuning reinforcement learning (RL) models has been challenging because of a lack of large scale off-the-shelf datasets as well as high variance in transferability among different environments. Recent work has looked at tackling offline RL from the perspective of sequence modeling with improved results as result of the introduction of the Transformer architecture. However, when the model is trained from scratch, it suffers from slow convergence speeds. In this paper, we look to take advantage of this formulation of reinforcement learning as sequence modeling and investigate the transferability of pre-trained sequence models on other domains (vision, language) when finetuned on offline RL tasks (control, games). To this end, we also propose techniques to improve transfer between these domains. Results show consistent performance gains in terms of both convergence speed and reward on a variety of environments, accelerating training by 3-6x and achieving state-of-the-art performance in a variety of tasks using Wikipedia-pretrained and GPT2 language models. We hope that this work not only brings light to the potentials of leveraging generic sequence modeling techniques and pre-trained models for RL, but also inspires future work on sharing knowledge between generative modeling tasks of completely different domains. Authors: Machel Reid, Yutaro Yamada, Shixiang Shane Gu 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/yann... 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 financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#wikipedia #reinforcementlearning #languagemodels Transformers have come to overtake many domain-targeted custom models in a wide variety of fields, such as Natural Language Processing, Computer Vision, Generative Modelling, and recently also Reinforcement Learning. This paper looks at the Decision Transformer and shows that, surprisingly, pre-training the model on a language-modelling task significantly boosts its performance on Offline Reinforcement Learning. The resulting model achieves higher scores, can get away with less parameters, and exhibits superior scaling properties. This raises many questions about the fundamental connection between the domains of language and RL. OUTLINE: 0:00 - Intro 1:35 - Paper Overview 7:35 - Offline Reinforcement Learning as Sequence Modelling 12:00 - Input Embedding Alignment & other additions 16:50 - Main experimental results 20:45 - Analysis of the attention patterns across models 32:25 - More experimental results (scaling properties, ablations, etc.) 37:30 - Final thoughts Paper: https://arxiv.org/abs/2201.12122 Code: https://github.com/machelreid/can-wik... My Video on Decision Transformer: https://youtu.be/-buULmf7dec Abstract: Fine-tuning reinforcement learning (RL) models has been challenging because of a lack of large scale off-the-shelf datasets as well as high variance in transferability among different environments. Recent work has looked at tackling offline RL from the perspective of sequence modeling with improved results as result of the introduction of the Transformer architecture. However, when the model is trained from scratch, it suffers from slow convergence speeds. In this paper, we look to take advantage of this formulation of reinforcement learning as sequence modeling and investigate the transferability of pre-trained sequence models on other domains (vision, language) when finetuned on offline RL tasks (control, games). To this end, we also propose techniques to improve transfer between these domains. Results show consistent performance gains in terms of both convergence speed and reward on a variety of environments, accelerating training by 3-6x and achieving state-of-the-art performance in a variety of tasks using Wikipedia-pretrained and GPT2 language models. We hope that this work not only brings light to the potentials of leveraging generic sequence modeling techniques and pre-trained models for RL, but also inspires future work on sharing knowledge between generative modeling tasks of completely different domains. Authors: Machel Reid, Yutaro Yamada, Shixiang Shane Gu Links: Merch: store.ykilcher.com 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/yann... 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 financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#mlnews #rsc #gpt3 Some things we've missed in recent weeks! OUTLINE: 0:00 - Intro & Overview 0:40 - Meta builds AI Research Supercluster (RSC) 2:25 - OpenAI trains GPT-3 to follow instructions 4:10 - Meta AI releases multilingual language models 4:50 - Google LaMDA dialogue models 5:50 - Helpful Things 8:25 - Training the alpha matte generator for Pixel 6 10:15 - Drones used to deter pigeons on buildings 11:05 - IBM sells some Watson Health assets for USD 1B Merch: store.ykilcher.com References: https://ai.facebook.com/blog/ai-rsc/?... https://openai.com/blog/instruction-f... https://cdn.openai.com/papers/Trainin... https://openai.com/blog/deep-reinforc... https://twitter.com/MetaAI/status/148... https://arxiv.org/pdf/2112.10668.pdf https://github.com/pytorch/fairseq/tr... https://ai.googleblog.com/2022/01/lam... https://arxiv.org/pdf/2201.08239.pdf https://evolutiongym.github.io/?utm_s... https://evolutiongym.github.io/all-tasks https://evolutiongym.github.io/docume... https://arxiv.org/pdf/2201.09863.pdf https://github.com/EvolutionGym https://huggingface.co/blog/sb3 https://twitter.com/Sentdex/status/14... https://github.com/lvwerra/trl?utm_so... https://ai.googleblog.com/2022/01/acc... https://polyhaven.com/hdris https://ieeexplore.ieee.org/document/... https://www.bloomberg.com/news/articl... https://archive.ph/xadf9 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/yann... 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 financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#mlnews #muzero #nerf Your regularly irregular updates on everything new in the ML world! Merch: store.ykilcher.com OUTLINE: 0:00 - Intro 0:15 - Sponsor: Weights & Biases 2:15 - Uber switches from XGBoost to Deep Learning for ETA prediction 5:45 - MuZero advances video compression 10:10 - Learned Soft Prompts can steer large language models 12:45 - Block-NeRF captures entire city blocks 14:15 - Neural Architecture Search considers underlying hardware 16:50 - Mega-Blog on Self-Organizing Agents 18:40 - Know Your Data (for Tensorflow Datasets) 20:30 - Helpful Things Sponsor: Weights & Biases https://wandb.me/yannic References: https://docs.wandb.ai/guides/integrat... https://colab.research.google.com/git... https://wandb.ai/borisd13/GPT-3/repor... Uber switches from XGBoost to Deep Learning for ETA prediction https://eng.uber.com/deepeta-how-uber... MuZero advances video compression https://deepmind.com/blog/article/MuZ... https://storage.googleapis.com/deepmi... Learned Soft Prompts can steer large language models https://ai.googleblog.com/2022/02/gui... https://aclanthology.org/2021.emnlp-m... Block-NeRF captures entire city blocks https://arxiv.org/abs/2202.05263 https://arxiv.org/pdf/2202.05263.pdf https://waymo.com/intl/zh-cn/research... Neural Architecture Search considers underlying hardware https://ai.googleblog.com/2022/02/unl... https://openaccess.thecvf.com/content... Mega-Blog on Self-Organizing Agents https://developmentalsystems.org/sens... https://flowers.inria.fr/ Know Your Data (for Tensorflow Datasets) https://knowyourdata-tfds.withgoogle.... https://knowyourdata.withgoogle.com/ Helpful Things https://twitter.com/casualganpapers/s... https://www.reddit.com/r/MachineLearn... https://arxiv.org/abs/2202.02435 https://github.com/vicariousinc/PGMax https://www.vicarious.com/posts/pgmax... https://diambra.ai/tournaments https://github.com/diambra/diambraArena https://www.youtube.com/watch?v=dw72P... https://gitlab.com/deepcypher/python-... https://python-fhez.readthedocs.io/en... https://joss.theoj.org/papers/10.2110... https://github.com/PyTorchLightning/m... https://torchmetrics.readthedocs.io/e... https://twitter.com/alanyttian/status... https://github.com/google/evojax https://arxiv.org/abs/2202.05008 https://www.reddit.com/r/MachineLearn... https://www.gymlibrary.ml/pages/api/#... 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/yann... 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 financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#mlnews #kilcher #withtheauthors Many of you have given me feedback on what you did and didn't like about the recent "with the authors" videos. Here's the result of that feedback and an outlook into the future. Merch: store.ykilcher.com 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/yann... 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 financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n