Frontiers | RM-SORN: a reward-modulated self-organizing recurrent neural network
Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-mod
Improving performance of recurrent neural network with relu nonlinearity
In recent years significant progress has been made in successfully training recurrent neural networks (RNNs) on sequence learning problems involving long range temporal dependencies. The progress has been made on three fronts: (a) Algorithmic improvements involving sophisticated optimization techniques, (b) network design involving complex hidden layer nodes and specialized recurrent layer connect
BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies
We propose BlackOut, an approximation algorithm to efficiently train massive recurrent neural network language models (RNNLMs) with million word vocabularies. BlackOut is motivated by using a discriminative loss, and we describe a new sampling strategy which significantly reduces computation while improving stability, sample efficiency, and rate of convergence. One way to understand BlackOut is to
Recurrent Neural Network Training with Dark Knowledge Transfer
Recurrent neural networks (RNNs), particularly long short-term memory (LSTM), have gained much attention in automatic speech recognition (ASR). Although some successful stories have been reported, training RNNs remains highly challenging, especially with limited training data. Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predict
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
In this paper, we propose a deep neural network architecture for object recognition based on recurrent neural networks. The proposed network, called ReNet, replaces the ubiquitous convolution+pooling layer of the deep convolutional neural network with four recurrent neural networks that sweep horizontally and vertically in both directions across the image. We evaluate the proposed ReNet on three w
DRAW: A Recurrent Neural Network For Image Generation
DRAW: A Recurrent Neural Network For Image Generation Karol Gregor KAROLG@GOOGLE.COM Ivo Danihelka DANIHELKA@GOOGLE.COM Alex Graves GRAVESA@GOOGLE.COM Danilo Jimenez Rezende DANILOR@GOOGLE.COM Daan Wierstra WIERSTRA@GOOGLE.COM Google DeepMind Abstract This paper introduces the Deep Recurrent Atten- tive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel s
わかるLSTM ～ 最近の動向と共に - Qiita
Machine Learning Advent Calendar 2015 第14日です。去年のAdvent Calendarで味をしめたので今年も書きました。質問、指摘等歓迎です。 この記事の目的 ここ2～3年のDeep Learningブームに合わせて、リカレントニューラルネットワークの一種であるLong short-term memory(LSTM)の存在感が増してきています。LSTMは現在Google Voiceの基盤技術をはじめとした最先端の分野でも利用されていますが、その登場は1995年とそのイメージとは裏腹に歴史のあるモデルでもあります。ところがLSTMについて使ってみた記事はあれど、詳しく解説された日本語文献はあまり見当たらない。はて、どういうことでしょうか。 本記事ではLSTMの基礎をさらいつつ、一体全体LSTMとは何者なのか、LSTMはどこに向かうのか、その中身をまとめ
GitHub - kjw0612/awesome-rnn: Recurrent Neural Network - A curated list of resources dedicated to RNN
Tensorflow - Python, C++ Get started, Tutorials Recurrent Neural Network Tutorial Sequence-to-Sequence Model Tutorial Tutorials by nlintz Notebook examples by aymericdamien Scikit Flow (skflow) - Simplified Scikit-learn like Interface for TensorFlow Keras : (Tensorflow / Theano)-based modular deep learning library similar to Torch char-rnn-tensorflow by sherjilozair: char-rnn in tensorflow Theano
DRAW: A Recurrent Neural Network For Image Generation
This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for gener
これもある意味Deep Learning，Recurrent Neural Network Language Modelの話 [MLAC2013_9日目] — KiyuHub
これもある意味Deep Learning，Recurrent Neural Network Language Modelの話 [MLAC2013_9日目] この投稿は Machine Learning Advent Calendar 2013 の9日目の記事です． 本日は，Neural Network(NN)を利用したLanguage Model(LM)，つまりNeural Network Language Model(NNLM)の一種であり， Recurrent Neural Network(RNN)を使ったRecurrent Neural Network Language Model(RNNLM)について説明します． RNNLMに行く前置きとしてざっくりいくつか用意したので，知ってそうな人は先へ進んでください． 前置きを入れているうちに長くなりすぎた...． しかも，そもそもNNの説明
![これもある意味Deep Learning，Recurrent Neural Network Language Modelの話 [MLAC2013_9日目] — KiyuHub](https://cdn-ak-scissors.b.st-hatena.com/image/square/cac50dfe53f692699058da4611c63ceb7fb568f7/height=288;version=1;width=512/http%3A%2F%2Fkiyukuta.github.io%2F_images%2Fneural_language_model.png)
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Recurrent neural network - Wikipedia
