新たなRNNと自然言語処理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
LSTMVis
- 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 by kjw0612
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
The Unreasonable Effectiveness of Recurrent Neural Networks
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
A Note on BPTT for LSTM LM
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
Photo-Real Talking Head with Deep Bidirectional LSTM - Microsoft Research
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
Photo-Real Talking Head with Deep Bidirectional LSTM - Microsoft Research
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
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
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THEANETS — Theanets 0.7.3 documentation
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This is a minimal implementation of training for a language model using long short-term memory (LSTM) neural networks
Recurrent Neural Networks
Supervised Sequence Labelling with Recurrent Neural Networks.pdf
Recurrent Neural Networks - Pfi seminar 20141030_rnn