RECURRENT NEURAL NETWORKS - FEEDBACK NETWORKS - LSTM RECURRENT NETWORK - FEEDBACK NEURAL NETWORK - RECURRENT NETS - FEEDBACK NETWORK - RECURRENT NET - - FEEDBACK NET
1. Our Open Source RNN & LSTM Software Librairies: Brainstorm; RNNLIB; Pybrain. 2. Upcoming RNN Book 3. Old version of this page (2003) LSTM in Journals: Jürgen Schmidhuber's page on Recurrent Neural Networks (updated 2017) Why use recurrent networks at all? And why use a particular Deep Learning recurrent network called Long Short-Term Memory or LSTM? 12. K. Greff, R. Srivastava, J. Koutnik, B. S
Scaling Recurrent Neural Network Language Models
This paper investigates the scaling properties of Recurrent Neural Network Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and address the questions of how RNNLMs scale with respect to model size, training-set size, computational costs and memory. Our analysis shows that despite being more costly to train, RNNLMs obtain much lower perplexities on standard benchmarks than
Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling
Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling Haşim Sak, Andrew Senior, Françoise Beaufays Google, USA {hasim,andrewsenior,fsb@google.com} Abstract Long Short-Term Memory (LSTM) is a specific recurrent neu- ral network (RNN) architecture that was designed to model tem- poral sequences and their long-range dependencies more accu- rately than conve
Recurrent Neural Networks for Language Processing - Microsoft Research
This project focuses on advancing the state-of-the-art in language processing with recurrent neural networks. We are currently applying these to language modeling, machine translation, speech recognition, language understanding and meaning representation. A special interest in is adding side-channels of information as input, to model phenomena which are not easily handled in other frameworks. A to
Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit
Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit Felix Weninger; 16(17):547−551, 2015. Abstract In this article, we introduce CURRENNT, an open-source parallel implementation of deep recurrent neural networks (RNNs) supporting graphics processing units (GPUs) through NVIDIA's Computed Unified Device Architecture (CUDA). CURRENNT supports uni- and bidirectional RNN
Recurrent Neural Network Regularization
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include langu
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
min-char-rnn.py & 쪪 `p 窪 """ Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) BSD License """ import numpy as np # data I/O data = open('input.txt', 'r').read() # should be simple plain text file chars = list(set(data)) data_size, vocab_size = len(data), len(chars) print 'data has %d characters, %d unique.' % (data_size, vocab_size) char_to_ix = { ch:i for i,ch in
Relation Classification via Recurrent Neural Network
Deep learning has gained much success in sentence-level relation classification. For example, convolutional neural networks (CNN) have delivered competitive performance without much effort on feature engineering as the conventional pattern-based methods. Thus a lot of works have been produced based on CNN structures. However, a key issue that has not been well addressed by the CNN-based method is
Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition
We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition. More recently, we have shown that the performance of sequence trained context dependent (CD) hidden Markov model (HMM) acoustic models using such LSTM RNNs can be equaled by sequence trained phone models initi
Optimizing and Contrasting Recurrent Neural Network Architectures
Recurrent Neural Networks (RNNs) have long been recognized for their potential to model complex time series. However, it remains to be determined what optimization techniques and recurrent architectures can be used to best realize this potential. The experiments presented take a deep look into Hessian free optimization, a powerful second order optimization method that has shown promising results,
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
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Long-term learning behavior in a recurrent neural network for sound recognition - IEEE Conference Publication
DRAW: A Recurrent Neural Network For Image Generation by Google DeepMind
https://clgiles.ist.psu.edu/papers/MLJ-finance.pdf
How does LSTM help prevent the vanishing (and exploding) gradient problem in a recurrent neural network?
Convolutional Neural Network Workbench
http://www.vision.cs.chubu.ac.jp/FLABResearchArchive/Bachelor/B13/Paper/fukui.pdf
Pedestrian Detection by Deep Convolutional Neural Network