2016/6/22にNLP-DLで話した際のスライドを一部編集したものです。 English ver. http://www.slideshare.net/hytae/recent-progress-in-rnn-and-nlp-63762080Read less
![新たなRNNと自然言語処理](https://cdn-ak-scissors.b.st-hatena.com/image/square/2d2861945e82449b153fb61eba4e621637136c7c/height=288;version=1;width=512/https%3A%2F%2Fcdn.slidesharecdn.com%2Fss_thumbnails%2Fnlpdl06forslidesharearabiki-160706014712-thumbnail.jpg%3Fwidth%3D640%26height%3D640%26fit%3Dbounds)
- Visual Analysis for Recurent Neural Networks Hendrik Strobelt, Sebastian Gehrmann, Bernd Huber, Hanspeter Pfister, Alexander M. Rush Recurrent neural networks, and in particular long short-term memory networks (LSTMs), are a remarkably effective tool for sequence processing that learn a dense black-box hidden representation of their sequential input. Researchers interested in better understandin
Awesome-rnn Recurrent Neural Network - A curated list of resources dedicated to RNN View on GitHub Download .zip Download .tar.gz Awesome Recurrent Neural Networks A curated list of resources dedicated to recurrent neural networks (closely related to deep learning). Maintainers - Jiwon Kim, Myungsub Choi We have pages for other topics: awesome-deep-vision, awesome-random-forest Contributing Please
May 21, 2015 There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simpl
This document provides an overview of backpropagation through time (BPTT) for long short-term memory (LSTM) language models. It describes the forward and backward passes for LSTM, including equations for calculating the input, forget, output and cell gates, as well as the cell state and hidden state. In the backward pass, it derives the equations for calculating the gradients with respect to the w
We propose to use deep bidirectional LSTM for audio/visual modeling in our photo-real talking head system. Abstract Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that was designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose to use deep bidirectional LSTM for audio/visual m
Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that is designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose to use deep bidirectional LSTM (BLSTM) for audio/visual modeling in our photo-real talking head system. An audio/visual database of a subject’s talking is firstly reco
RNNLIB is a recurrent neural network library for sequence learning problems. Applicable to most types of spatiotemporal data, it has proven particularly effective for speech and handwriting recognition. full installation and usage instructions given at http://sourceforge.net/p/rnnl/wiki/Home/ Features LSTMMultidimensional recurrent neural networksConnectionist temporal classificationAdaptive weigh
cvpaper.challengeはコンピュータビジョン分野の今を映し、トレンドを創り出す挑戦です。論文サマリ・アイディア考案・議論・実装・論文投稿に取り組み、凡ゆる知識を共有しています。 http://xpaperchallenge.org/cv/ 本資料はViEW2021チュートリアルセッション「最新研究の始め方」のプレゼン素材です。また、xpaper.challengeの2020年末ワークショップとしてプレゼンした「研究効率化Tips」の拡張版です。本資料では3社12研究室300ページにわたるノウハウの詰め合わせです。 VIEW2021のチュートリアルセッションでは時間の制限があるため、こちらの資料から一部抜粋して発表を行うことになりますが、VIEW2021チュートリアルセッションの方にも足を運んでいただければ幸いです。 VIEW2021チュートリアルセッション:http://vie
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