Journal ArticleDOI
Learning long-term dependencies with gradient descent is difficult
TLDR
This work shows why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases, and exposes a trade-off between efficient learning by gradient descent and latching on information for long periods.Abstract:
Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in the input/output sequences span long intervals. We show why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases. These results expose a trade-off between efficient learning by gradient descent and latching on information for long periods. Based on an understanding of this problem, alternatives to standard gradient descent are considered. >read more
Citations
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Proceedings ArticleDOI
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
Xuezhe Ma,Eduard Hovy +1 more
TL;DR: This paper used a combination of bidirectional LSTM, CNN and CRF for sequence labeling tasks, and achieved state-of-the-art performance on both datasets for POS tagging and CoNLL 2003 corpus for NER.
Book
Supervised Sequence Labelling with Recurrent Neural Networks
TL;DR: A new type of output layer that allows recurrent networks to be trained directly for sequence labelling tasks where the alignment between the inputs and the labels is unknown, and an extension of the long short-term memory network architecture to multidimensional data, such as images and video sequences.
Proceedings ArticleDOI
Beyond short snippets: Deep networks for video classification
Joe Yue-Hei Ng,Matthew Hausknecht,Sudheendra Vijayanarasimhan,Oriol Vinyals,Rajat Monga,George Toderici +5 more
TL;DR: In this article, a recurrent neural network that uses Long Short-Term Memory (LSTM) cells which are connected to the output of the underlying CNN was proposed to model the video as an ordered sequence of frames.
Proceedings ArticleDOI
Conditional Random Fields as Recurrent Neural Networks
Shuai Zheng,Sadeep Jayasumana,Bernardino Romera-Paredes,Vibhav Vineet,Zhizhong Su,Dalong Du,Chang Huang,Philip H. S. Torr +7 more
TL;DR: In this article, a new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling is introduced.
Proceedings ArticleDOI
LSTM Neural Networks for Language Modeling.
TL;DR: This work analyzes the Long Short-Term Memory neural network architecture on an English and a large French language modeling task and gains considerable improvements in WER on top of a state-of-the-art speech recognition system.
References
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Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book ChapterDOI
Learning internal representations by error propagation
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Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Journal ArticleDOI
A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams,David Zipser +1 more
TL;DR: The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks.
Journal ArticleDOI
Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm—Corrigenda for this article is available here
TL;DR: A new global optimization algorithm for functions of continuous variables is presented, derived from the “Simulated Annealing” algorithm recently introduced in combinatorial optimization, which is quite costly in terms of function evaluations, but its cost can be predicted in advance, depending only slightly on the starting point.