Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
Note: This paper describes an older version of DeepLIFT. See https://arxiv.org/abs/1704.02685 for the newer version. Original abstract follows: The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Learning Important FeaTures), an efficient and effective method for computing importance scores in a
AI、ついに人類最後の砦である｢Flappy Bird｣をも攻略
AI、ついに人類最後の砦である｢Flappy Bird｣をも攻略2016.05.09 09:00 渡邊徹則 もはや人類に為す術はないのか…。 囲碁において、コンピューターが人間に勝つのは｢あと10年かかる｣といわれていました。しかしご存じの通り、AIが世界のトッププロを破ったのはつい先日のことです。 そしておそるべきことに、AIはついに｢人類最後の砦｣ともいうべき激ムズゲーム｢Flappy Bird｣をも攻略してしまったのです。 この動画は現在、Googleが開催している学生向けの開発イベント｢Google Summer of Code｣に参加中のYen Chen Linさんがアップしたもの。 最強の囲碁ソフト｢AlphaGo｣を生み出したのは、Googleの子会社であるDeep Mind社でしたが、こちらで使われている画像認識によってゲームを攻略するAI、｢DQN（Deep Q-lear
Diederik P. (Durk) Kingma
Durk Kingma Diederik P. Kingma Publications | Brief Bio | PhD Thesis | Demos | Links | Honors Publications See my Google Scholar profile for an up-to-date list. Brief Bio I'm a computer scientist and researcher, with a focus on scalable methods for machine learning and generative modeling. My contributions include the Variational Autoencoder (VAE), the Adam optimizer, Glow, and Variational Diffusi
End to End Learning for Self-Driving Cars
We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with unclear visual guidance such as in parking l
About NVIDIA
NVIDIA DGX-1はサーバー２５０台分のスループットを実現し、人工知能で求められる大量コンピューティングに対応 米国カリフォルニア州サンノゼ—GPUテクノロジ・カンファレンス—2016年4月5日― NVIDIA（本社：米国カリフォルニア州サンタクララ、社長兼CEO：ジェンスン・フアン（Jen-Hsun Huang）、Nasdaq：NVDA）は本日、人工知能（AI）で要求される無制限のコンピューティングに対応する世界初のディープ・ラーニング・スーパーコンピュータ、NVIDIA® DGX-1™を初公開しました。 NVIDIA DGX-1は、ディープ・ラーニングに特化して設計された初のシステムで、ハードウェア、ディープ・ラーニング・ソフトウェア、および開発ツールを余すところなく統合し、開発を迅速かつ容易にします。このシステムはすぐに稼働可能な状態で提供され、新世代のGPUアクセラレータを搭
Neural Nets Back to the Future @ ICML 16
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Neural Networks Are Impressively Good At Compression
Neural Networks Are Impressively Good At Compression by Malte Skarupke I’m trying to get into neural networks. There have been a couple big breakthroughs in the field in recent years and suddenly my side project of messing around with programming languages seemed short sighted. It almost seems like we’ll have real AI soon and I want to be working on that. While making my first couple steps into th
GNU Rates GitHub & SourceForge With "F" Ratings - Phoronix
GNU Rates GitHub & SourceForge With "F" Ratings Written by Michael Larabel in GNU on 25 April 2016 at 04:34 PM EDT. 46 Comments The Free Software Foundation today announced their evaluations of major code repository-hosting services per the standards of the GNU Ethical Criteria for Code Repositories. Ranked as part of the code repository platforms were GNU Savannah, GitLab, GitHub, and SourceForge
Inside OpenAI, Elon Musk's Wild Plan to Set Artificial Intelligence Free
How many dollars is "borderline crazy"? Two years ago, as the market for the latest machine learning technology really started to heat up, Microsoft Research vice president Peter Lee said that the cost of a top AI researcher had eclipsed the cost of a top quarterback prospect in the National Football League---and he meant under regular circumstances, not when two of the most famous entrepreneurs i
Human-level control through deep reinforcement learning
LETTER doi:10.1038/nature14236 Human-level control through deep reinforcement learning Volodymyr Mnih1 *, Koray Kavukcuoglu1 *, David Silver1 *, Andrei A. Rusu1 , Joel Veness1 , Marc G. Bellemare1 , Alex Graves1 , Martin Riedmiller1 , Andreas K. Fidjeland1 , Georg Ostrovski1 , Stig Petersen1 , Charles Beattie1 , Amir Sadik1 , Ioannis Antonoglou1 , Helen King1 , Dharshan Kumaran1 , Daan Wierstra1 ,
Deep Learning Overview & Visualizing What Deep Neural Networks Learn
Jeff Clune introduces deep learning, describes how it is changing many fields of science and sectors of the economy, and then describes the work he has been involved in regarding how to visualize what feature each neuron in a neural network has learned to detect. For all of the papers, source code, and more video research summaries, see http://EvolvingAI.org Note: almost all of this work was do
NVIDIA Deep Learning Day 2016 Spring - 資料一覧 - connpass
終了 2016/04/27（水） 10:00〜 NVIDIA Deep Learning Day 2016 Spring Tesla P100, NVIDIA DGX-1の最新情報 yukofuji 他 愛知県名古屋市中区栄3-18-1
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification
We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities. After deriving its properties, we show how its Jacobian can be efficiently computed, enabling its use in a network trained with backpropagation. Then, we propose a new smooth and convex loss function which is the sparsemax analogue of the logistic loss. We reveal an unexpect
A brand new dance partner: Teaching AI to dance
As data scientists gradually inch machines toward human levels of intelligence, they’ve begun to tackle some very human endeavours. At Peltarion, an artificial intelligence (AI) expert and dance choreographer teamed up to find out if an AI model can not only learn a dancer’s style, but create its very own artistically compelling choreography. Art has always worked hand in hand with technology to p
数学的基礎から学ぶ Deep learning (with Python) Vol. 5【MPS 横浜 第5回】
MPS横浜 概要 MPSは、「みんなでわいわいソフトウェア作りながら技術を磨いて実績を作ろう!」をコンセプトに活動しています。プログラミング玄人とも初心者もみんなで協力して作品作りを行い、できあがった作品は共同制作のオープンソース・ソフトウェアとして、世の中に出します。 MPS横浜は「筋電位とDeep learning を用いた簡単なロボットアームを作ってみよう!」をテーマに発足しました。 前回から数回にわたって行う勉強会は、そのための Deep learning と電子回路に関する知識の実践的な獲得を目的とします。前提知識をあまり仮定せず 、基礎の基礎から学べるようにしてゆきます (高校二年生程度の数学の知識と if や for 文など Python の経験を少し仮定しますが、なくても是非チャレンジしてみてください ! )。 勉強会の後はみんなでご飯を食べにいきます ! 今回の予定 数学
Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification
Abstract: We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features. Based on Convolutional Neural Networks, our deep network features a fusion layer that allows us to elegantly merge local information dependent on small image patches with global priors computed using the entire image. The entire framework, including the globa
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Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video
TechCrunch | Startup and Technology News
Image Colorization Using a Deep Convolutional Neural Network
NVIDIA、Pascal搭載の深層学習用スパコン「DGX-1」の国内販売開始 ～「Deep Learning Day 2016 Spring」講演
