2017/09/03 Deep Learning Acceleration勉強会@DeNAã§ã®ç™ºè¡¨è³‡æ–™ã§ã™ã€‚
ウェブブラウザå‘ã‘深層å¦ç¿’モデル高速実行フレームワーク「WebDNNã€
2017/09/03 Deep Learning Acceleration勉強会@DeNAã§ã®ç™ºè¡¨è³‡æ–™ã§ã™ã€‚
An Overview of Multi-Task Learning for Deep Learning
An Overview of Multi-Task Learning in Deep Neural Networks Multi-task learning is becoming more and more popular. This post gives a general overview of the current state of multi-task learning. In particular, it provides context for current neural network-based methods by discussing the extensive multi-task learning literature. Note: If you are looking for a review paper, this blog post is also av
論文解説 Memory Networks - ディープラーニングブãƒã‚°
ã“ã‚“ã«ã¡ã¯ï¼ŒRyobot (りょã¼ã£ã¨) ã§ã™ï¼Ž æ¦‚è¦ ã€Œãƒ¡ãƒ¢ãƒªãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã€ã¯ä»£è¡¨çš„ãªè¨˜æ†¶è£…置付ãニューラルãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã§ã‚る． 本稿ã§ã¯ãƒ¡ãƒ¢ãƒªãƒ¢ãƒ‡ãƒ« (記憶装置付ãニューラルãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯) ã‚’ã„ãã¤ã‹æ¦‚説ã—，論文 2 ç´™ (1) Memory Networks, (2) Towards AI-Complete Question Answering ã®ç†è«–çš„ãªè¨˜è¿°ã‚’全文翻訳ã—ã¦è£œè¶³èª¬æ˜Žã—ã¦ã„る． 目次 メモリモデルã®æ¦‚説 Memory Networks (MemNN) 1 メモリãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã®æ¦‚è¦ 2 基本モデル 3 拡張モデル 4 実験 Towards AI-Complete Question Answering (bAbI task) 1 メモリãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã®æ‹¡å¼µ 2 bAbI タスク 3 実験 é•·æ–‡ã§ã‚る．ã–ã£ãり知るã ã‘ãªã‚‰ã€Œãƒ¡ãƒ¢ãƒªãƒ¢ãƒ‡ãƒ«ã®æ¦‚説ã€ã¨ Memory Networks
Understanding LSTM Networks -- colah's blog
Recurrent Neural Networks Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence. Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to
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Linguistic Knowledge as Memory for Recurrent Neural Networks_論文紹介
GitHub - dennybritz/chatbot-retrieval: Dual LSTM Encoder for Dialog Response Generation
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