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Computing Your Skill
Summary: I describe how the TrueSkill algorithm works using concepts you’re already familiar with. TrueSkill is used on Xbox Live to rank and match players and it serves as a great way to understand how statistical machine learning is actually applied today. I’ve also created an open source project where I implemented TrueSkill three different times in increasing complexity and capability. In addi
Strongly Convex
In the past 50+ years of convex optimization research, a great many algorithms have been developed, each with slight nuances to their assumptions, implementations, and guarantees. In this article, I'll give a shorthand comparison of these methods in terms of the number of iterations required to reach a desired accuracy \(\epsilon\) for convex and strongly convex objective functions. Below, methods
Reddit - Dive into anything
https://preview.redd.it/8eed729klj771.png?width=1058&format=png&auto=webp&s=584d2e744d436d4b75e2e40d69e58e6d14cbcd9a It is said in the lectures here at 11:30 that because the importance sampling weight is going to zero exponentially fast then the variance of the gradient will also go to infinity exponentially fast. Why is that? I do not understand what causes this problem?
天才肌の巣窟！変拍子ブレイクコアの魅力 – OthermanRecords Official Blog
みなさんこんにちは、ブレイクコア邪神です。 只今開催中、一般から選抜されたヤバイやつらが、ヤバイブレイクコアでバトルを繰り広げる #天一アーメン2。楽しんでいただけておりますでしょうか…？ #天一アーメン2 準決勝二回戦「7拍子」ー LIKEの一番多いやつが勝ち！ そんな #天一アーメン2 のお題で「7拍子」と言うのが出たのですが、今回はそちらにちなんで変拍子ブレイクコア特集をやってみようかと思いまして、この記事を書きました。 記事の構成は、前半が代表格3人と海外勢の紹介、後半が日本勢の楽曲の紹介となっております。 毎度ながら、この記事がまだ見ぬアーティストや楽曲との出会いになりましたら、嬉しいですので、是非いろいろdigってみてください…！ YouTubeは画像クリックで再生できます 変拍子の有名所その1 まず、変拍子ブレイクコアの主なイメージとして、天才肌がやる分野、7拍子、といったも
GTA V + Universe
The Universe integration with Grand Theft Auto V , built and maintained by Craig Quiter's DeepDrive project, is now open-source. To use it, you'll just need a purchased copy of GTA V, and then your Universe agent will be able to start driving a car around the streets of a high-fidelity virtual world. GTA V in Universe gives AI agents access to a rich, 3D world. This video shows the frames fed to t
Neural networks and deep learning
Neural Networks and Deep Learning What this book is about On the exercises and problems Using neural nets to recognize handwritten digits How the backpropagation algorithm works Improving the way neural networks learn A visual proof that neural nets can compute any function Why are deep neural networks hard to train? Deep learning Appendix: Is there a simple algorithm for intelligence? Acknowledge
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nips16interaction
Deep Learning for Action and Interaction, NIPS 2016, Area 3 Images (left to right): Pinto & Gupta ICRA '16, Blundell et al. '16, Chen et al. ICCV '15, Levine et al. ISER '16, Wang et al. '16 In conjunction with NIPS 2016, Barcelona. Organizers: Chelsea Finn, Raia Hadsell, Dave Held, Sergey Levine, Percy Liang Videos of the workshop are now available here. This workshop is located in Area 3 of the
Universe
Universe allows an AI agent to use a computer like a human does: by looking at screen pixels and operating a virtual keyboard and mouse. We must train AI systems on the full range of tasks we expect them to solve, and Universe lets us train a single agent on any task a human can complete with a computer. In April, we launched Gym, a toolkit for developing and comparing reinforcement learning (RL)
Cambridge Machine Learning Group Publications
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Uncertainty in Deep Learning (PhD Thesis) | Yarin Gal - Blog | Cambridge Machine Learning Group
Function draws from a dropout neural network. This new visualisation technique depicts the distribution over functions rather than the predictive distribution (see demo below). So I finally submitted my PhD thesis (given below). In it I organised the already published results on how to obtain uncertainty in deep learning, and collected lots of bits and pieces of new research I had lying around (wh
NIPS 2016 Proceedings
Advances in Neural Information Processing Systems 29 (NIPS 2016) The papers below appear in Advances in Neural Information Processing Systems 29 edited by D.D. Lee and M. Sugiyama and U.V. Luxburg and I. Guyon and R. Garnett. They are proceedings from the conference, "Neural Information Processing Systems 2016." Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much Bryan
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Conformal Prediction - A Tiny Tutorial on Predicting with =1=Confidence
NIPS 2016 Schedule
Jan Leike
Videolectures
Approximate Inference - Call for Papers
Bayesian Machine Learning for Controlling Autonomous Systems
Marc Deisenroth