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Connection Science, Vol. 11, No. 1, 1999, 5± 40 A Recurrent Neural Network that Learns to Count PAUL RODRIGUEZ, JANET WILES & JEFFREY L. ELMAN Parallel distributed processing (PDP) architectures demonstrate a potentially radical alternative to the traditional theories of language processing that are based on serial computational models. However, learning complex structural relationships in tempora
Answer (1 of 2): Good question. RNN is a type of HMM, that is every RNN is a HMM but not every HMM is an RNN. I think the main difference is twofold, every RNN is a HMM + two assumptions/constraints : 1. Bayesian vs Maximum likelihood approximation (of the transition matrix) - when it comes to...
Recurrent Neural Network Language Models
Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designed to address the vanishing and exploding gradient problems of conventional RNNs. Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences. They have been successfully used for sequence labeling and sequence prediction tasks, such as handwriting rec
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
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 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
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 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
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
min-char-rnn.py ��U """ 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 enum
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
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
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,
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