Visualizing memorization in RNNs
Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use short-term or long-term contextual understanding. This connectivity visualization shows how strongly previous input characters influence the current target character in an autocomplete problem. For example, in the prediction of “grammar†the GRU RNN initially uses long-term memorization but as more cha
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(Fig. 1 from Rumelhart, Hinton & Williams, Nature, 1986) ã“ã‚Œã¯ã¡ã‚‡ã£ã¨ã—ãŸå°ãƒã‚¿ã§ã™ã€‚僕自身ã¯ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ (Neural Network, NN)ã®å¦è¡“的専門家ã§ã‚‚ãªã‘ã‚Œã°æ³ã—ã¦ã‚„NNã®ç ”究å²å®¶ã§ã‚‚ãªã„ã®ã§ã€ãŸã‹ã ã‹åƒ•è‡ªèº«ãŒã‹ã¤ã¦è„³ã®ç ”究者ã ã£ãŸé ƒã«æŠŠæ¡ã—ã¦ã„ãŸäº‹ç”±ã«åŸºã¥ã„ã¦ã€æ¥µã‚ã¦ã„ã„åŠ æ¸›ãªç§è¦‹ã‚’書ã„ã¦ã„ã‚‹ã«éŽãŽãªã„ã“ã¨ã‚’予ã‚ãŠæ–ã‚Šã—ã¦ãŠãã¾ã™ã€‚よã£ã¦ã€ã“ã®è¾ºã®äº‹æƒ…ã«è©³ã—ã„æ–¹ã„らã£ã—ゃã„ã¾ã—ãŸã‚‰ã€å¾Œå¦ã®ãŸã‚ã«ã‚‚是éžã”é æ…®ãªãツッコミを入れã¦ä¸‹ã•ã‚‹ã¨æœ‰é›£ã„ã§ã™m(_ _)m 先日ã®ã“ã¨ã§ã™ãŒã€@tmaeharaã•ã‚“ãŒã“ã‚“ãªã“ã¨ã‚’å‘Ÿã„ã¦ãŠã‚‰ã‚Œã¾ã—ãŸã€‚ オリジナル論文 https://t.co/kXfu8jIat3 ã“ã‚Œã§ã™ï¼Žæœ¬å½“ã«ãŸã ãƒã‚§ã‚¤ãƒ³ãƒ«ãƒ¼ãƒ«ã§å¾®åˆ†ã—ã¦å‹¾é…法ã—ã¦ã„ã‚‹ã ã‘ã«ã—ã‹è¦‹ãˆãªã„……．— â„¢ (@tmaehara
論文紹介 Understanding Batch Normalization - ã˜ã‚“ã¹ãˆã–ã‚ã®ãƒŽãƒ¼ãƒˆ
今回ã¯ã€NIPS2018ã«æŠ•ç¨¿ã•ã‚ŒãŸUnderstanding Batch Normalizationã¨ã„ã†è«–文をèªã‚“ã ã®ã§ã€ç´¹ä»‹ã—ã¦ã„ããŸã„ã¨æ€ã„ã¾ã™ã€‚ã“ã®è«–æ–‡ã¯ã€ãªãœãƒãƒƒãƒæ£è¦åŒ–（Batch Normalization）ãŒå¦ç¿’ã«åŠ¹æžœçš„ãªã®ã‹ã‚’実証的ãªã‚¢ãƒ—ãƒãƒ¼ãƒã§æ¤œè¨¼ã—ãŸè«–æ–‡ã§ã™ã€‚ ã“ã®è¨˜äº‹ã¯ã€ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã®åŸºç¤Žï¼ˆå…¨çµåˆå±¤ã‚„畳ã¿è¾¼ã¿å±¤ï¼‰ã‚’ç†è§£ã—ã¦ã„る方を対象ã«ã—ã¦ã„ã¾ã™ã€‚ã¾ãŸã€å¯èƒ½ãªé™ã‚Šãƒãƒƒãƒæ£è¦åŒ–を知らãªã„æ–¹ã§ã‚‚ç†è§£ã§ãるよã†ã«ã—ã¦ã„ã¾ã™ã€‚ã“ã®è¨˜äº‹ã‚’èªã¿çµ‚ã‚ã‚‹ã“ã‚ã«ã¯ã€ã€Œãªãœãƒãƒƒãƒæ£è¦åŒ–ãŒå¦ç¿’ã«åŠ¹æžœçš„ãªã®ã‹ã€ãŒåˆ†ã‹ã‚‹ã‚ˆã†ã«ãªã£ã¦ã„ã¾ã™ã€‚ ニューラルãƒãƒƒãƒˆã®åŸºç¤Žã¯ä»¥ä¸‹ã®è¨˜äº‹ã§ç´¹ä»‹ã—ã¦ã„ã¾ã™ã€‚ ニューラルãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯å…¥é–€ KelpNetã§CNN ã“ã®è¨˜äº‹ã¯è«–文をè¦ç´„ã—ãŸã‚‚ã®ã«èª¬æ˜Žã‚’åŠ ãˆãŸã‚‚ã®ã¨ãªã£ã¦ã„ã¾ã™ã€‚記事内ã§ï¼‘人称ã§èªžã‚‰ã‚Œã¦ã„ã‚‹æ–‡ç« ã«ã¤ã„ã¦ã¯ã€å¤šããŒè«–æ–‡ã®ä¸»å¼µã¨ãªã£ã¦ã„ã¾ã™
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Weekly Machine Learning #75
News: ml5.jsã¯ã€TensorFlow.jsをベースã«ã‚ˆã‚Šæ©Ÿæ¢°å¦ç¿’機能を簡å˜ã«å–り入れられるよã†ã«ã—ãŸãƒ©ã‚¤ãƒ–ラリã«ãªã£ã¦ã„ã¾ã™ã€‚ã“ã¡ã‚‰ã‚’使ãˆã°ã€Webカメラã§æ‰‹ã®å‹•ãã«åˆã‚ã›ã¦Pongã®ã‚²ãƒ¼ãƒ ã‚’ã™ã‚‹ã¨ã„ã£ãŸã“ã¨ã‚‚ã§ããã†ã§ã™ã€‚Depth First Learningã¯ä¸€æœ¬ã®è«–文をä¸å¯§ã«èªã‚“ã§ã„ãよã†ãªå½¢ã§ã€å‰æ知è˜ã‚„å‚考文献ã«ã¤ã„ã¦ç´°ã‹ã„解説ãŒã—ã£ã‹ã‚Šã¨è¡Œã‚ã‚Œã¦ã„ã¾ã™ã€‚ Articles: DeepProbLog~ã‚„To Trust Or Not~ã¯ã€æ©Ÿæ¢°å¦ç¿’ãŒå®Ÿéš›ã«ã‚·ã‚¹ãƒ†ãƒ ã«å…¥ã£ã¦ããŸã¨ãã«ã©ã†æ‰±ã†ã¹ãã‹ã«ã¤ã„ã¦ç¤ºå”†ã‚’与ãˆã¦ãã‚Œã‚‹ç ”ç©¶ã§ã™ã€‚Relational inductive biases~ã¯ã‚°ãƒ©ãƒ•ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆæ™‚代ã®åˆ°æ¥ã‚’予感ã•ã›ã‚‹ç ”究ã«ãªã£ã¦ã„ã¾ã™ã€‚数年後振り返ã£ãŸã¨ãã«ã€ã“ã®è«–æ–‡ãŒè»¢æ›ç‚¹ã ã£ãŸã¨æŒ¯ã‚Šè¿”られるå¯èƒ½æ€§ã‚‚ã‚ã‚‹ã¨æ€ã„ã¾ã™ã€‚
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1. 概è¦ä¸‹è¨˜ã®arXiv論文を紹介ã—ã¾ã™ã€‚ Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao (2018). Block Coordinate Descent for Deep Learning: Unified Convergence Guarantees.arXiv:1803.00225 ç¾æ™‚点ã§ã¯æŠ•ç¨¿ã•ã‚Œã¦é–“ã‚‚ãªã„è«–æ–‡ã§ã™ãŒã€å€‹äººçš„ã«ã¯æ©Ÿæ¢°å¦ç¿’ã®è«–文をèªã‚“ã§ã„ã¦ä¹…々ã«æ¥½ã—ã„æ°—æŒã¡ã«ãªã‚Œã¾ã—ãŸã€‚ è«–æ–‡ã®æ案手法ã¯gradient-free methodã¨å‘¼ã°ã‚Œã‚‹æ‰‹æ³•ã®ä¸€ç¨®ãªã®ã§ã€æœ¬è¨˜äº‹ã¯ãã®ã‚ãŸã‚Šã®ãƒ¬ãƒ“ューも少ã—å…¼ãã¾ã™ã€‚ 2. 勾é…法ã®åŽæŸæ¡ä»¶ãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«ãƒãƒƒãƒˆã®æ§‹é€ ã‚’ã²ã¨ã¤å›ºå®šã—ã€ãã®æ§‹é€ を使ã£ã¦è¡¨ã›ã‚‹é–¢æ•°ã®å…¨ä½“ã‚’ $\mathcal{F}$ ã¨æ›¸ãã¾ã™ã€‚ニューラルãƒãƒƒãƒˆã®å¦ç¿’ã¨ã¯ã€ä¸Žãˆã‚‰ã‚ŒãŸæ失を最å°åŒ–ã™ã‚‹é–¢æ•°ã‚’見ã¤ã‘ã‚‹ã“ã¨ã§
論文解説 Attention Is All You Need (Transformer) - ディープラーニングブãƒã‚°
ã“ã‚“ã«ã¡ã¯ Ryobot (りょã¼ã£ã¨) ã§ã™ï¼Ž 本紙㯠RNN ã‚„ CNN を使ã‚ãš Attention ã®ã¿ä½¿ç”¨ã—ãŸãƒ‹ãƒ¥ãƒ¼ãƒ©ãƒ«æ©Ÿæ¢°ç¿»è¨³ Transformer ã‚’æ案ã—ã¦ã„る． ã‚ãšã‹ãªè¨“ç·´ã§åœ§å€’的㪠State-of-the-Art ã‚’é”æˆã—，è¯éº—ã«ã‚¿ã‚¤ãƒˆãƒ«å›žåŽã—ãŸï¼Ž ã¾ãŸæ³¨æ„ã‚’éžå¸¸ã«ã‚·ãƒ³ãƒ—ルãªæ•°å¼ã«ä¸€èˆ¬åŒ–ã—ãŸã†ãˆã§ï¼ŒåŠ 法注æ„・内ç©æ³¨æ„・ソースターゲット注æ„・自己注æ„ã«åˆ†é¡žã—ãŸï¼Žã“ã®ã†ã¡è‡ªå·±æ³¨æ„ã¯ã‹ãªã‚Šæ±Žç”¨çš„ã‹ã¤å¼·åŠ›ãªæ‰‹æ³•ã§ã‚ã‚Šä»–ã®ã‚らゆるニューラルãƒãƒƒãƒˆã«è»¢ç”¨ã§ãる． WMT'14 ã® BLEU スコアã¯è‹±ä»: 41.0, 英独: 28.4 ã§ç¬¬ 1 ä½ Attention Is All You Need [Åukasz Kaiser et al., arXiv, 2017/06] Transformer: A Novel Neural Network Architecture f
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PyTorchã§DQNを実装ã—㟠| moskomule log
ã¯ã˜ã‚ã« DQN(Deep Q Network)㯠Minh et al. 20151（以下論文）ã§ç™»å ´ã—ãŸæ·±å±¤å¼·åŒ–å¦ç¿’ã®å…ˆé§†ã‘ã§ã™ï¼ŽAtariã®ã‚²ãƒ¼ãƒ ã§éžå¸¸ã«é«˜ã„得点を修ã‚ã‚‹ã¨ã„ã†ãƒ‘フォーマンスã§æœ‰åã«ãªã‚Šã¾ã—ãŸï¼Ž 9æœˆé ƒã«å¼·åŒ–å¦ç¿’ã®å‹‰å¼·ã‚’ã—ãŸéš›ã«å®Ÿè£…ã—ã¦ã¿ãŸã®ã§ã™ãŒï¼Œä¸€å‘ã«å¦ç¿’ãŒé€²ã¾ãšæ”¾ç½®ã—ã¦ã„ãŸã®ã§ã™ãŒï¼Œæœ€è¿‘Implementing the Deep Q-Network 2ã‚’èªã¿å†é–‹ã—ã¦ã¿ãŸã¨ã“ã‚，動ã„ã¦ã—ã¾ã£ãŸã®ã§ï¼Œã“ã®è¨˜äº‹ã‚’書ãã“ã¨ã«ãªã‚Šã¾ã—ãŸï¼Ž 今回ã®å®Ÿè£…ã¯ã“ã¡ã‚‰ã«ã‚ã‚Šã¾ã™ï¼Ž 強化å¦ç¿’ã¨ã¯ David Silver先生ã«èžãã¾ã—ょã†ï¼ŽãŸã ã—ã“ã®è¬›ç¾©ã§ã¯æ·±å±¤å¼·åŒ–å¦ç¿’ã¯æ‰±ã‚ã‚Œã¦ã„ã¾ã›ã‚“． Deep Q-Networkã¨ã¯ 論文をèªã¿ã¾ã—ょã†ï¼ŽQ-Learningã®å¿œç”¨ã§ï¼Œè¤‡é›‘ã§ã¯ã‚ã‚Šã¾ã›ã‚“ãŒï¼Œå¦ç¿’を安定ã•ã›ã‚‹ãŸã‚ã®å·¥å¤«ãŒå„所ã«ã‚ã‚‹ã®ã§è¦‹é€ƒã™ã¨å‹•ã‹ãªã„よã†ã§ã™ï¼Ž DQNã®å¦ç¿’アルゴリ
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Chainerã®ã‚½ãƒ¼ã‚¹ã‚’解æžã€‚é †ä¼æ’ã¨é€†ä¼æ’ã®ä»•çµ„ã¿ | コード７区
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Double Backpropagationã«ã¤ã„㦠| moskomule log
ã¯ã˜ã‚ã« PyTorch v0.2ã§ã¯"Higher order gradients†(double backpropagation)ãŒã‚µãƒãƒ¼ãƒˆã•ã‚Œã¾ã—ãŸï¼ŽChainerã‚‚v3ã«ãŠã„ã¦ã“ã‚ŒãŒã‚µãƒãƒ¼ãƒˆã•ã‚Œã¾ã™ï¼Žä»Šå›žChainer Meetupã®è³‡æ–™ã‚’èªã‚“ã§é›°å›²æ°—ãŒåˆ†ã‹ã£ãŸã®ã§ã¾ã¨ã‚ã¾ã—ãŸï¼Ž Comparison of deep learning frameworks from a viewpoint of double backpropagation Chainer v3 ç†è€…ã¯é•·ãdouble backpropagationã¨ã„ã†å称ã‹ã‚‰ \[\mathrm{loss}\longrightarrow \frac{\partial^2 \mathrm{loss}}{\partial x_i \partial x_j} \] ã¨æ€ã„込んã§ã„ã¾ã—ãŸï¼Žãã†æ€ã£ã¦ã„ã‚‹ã®ã§documentã‚’èªã‚“ã§ã‚‚
A Neural Parametric Singing Synthesizer
Sound examples Contact: {merlijn.blaauw, jordi.bonada}@upf.edu [extended journal paper] Published: 18 December 2017. [original paper] [poster] Presented at Interspeech 2017, August 20-24, 2017, Stockholm, Sweden. [voice cloning demos] To be presented at ICASSP 2019, May 12-17, 2019, Brighton, UK. Demos English male voice (M1) - Take the A train In the following examples only timbre is generated by
My Neural Network isn't working! What should I do?
19/08/2017 So you're developing the next great breakthrough in deep learning but you've hit an unfortunate setback: your neural network isn't working and you have no idea what to do. You go to your boss/supervisor but they don't know either - they are just as new to all of this as you - so what now? Well luckily for you I'm here with a list of all the things you've probably done wrong and compiled
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Lifted Neural Networks
GitHub - vahidk/EffectiveTensorflow: TensorFlow tutorials and best practices.
dynet/Autobatching.ipynb at master · clab/dynet · GitHub
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