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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Posted Content
Learning a Recurrent Visual Representation for Image Caption Generation
Xinlei Chen,C. Lawrence Zitnick +1 more
TL;DR: This paper uses a novel recurrent visual memory that automatically learns to remember long-term visual concepts to aid in both sentence generation and visual feature reconstruction and evaluates the approach on several tasks.
Journal ArticleDOI
Neural circuits as computational dynamical systems
TL;DR: This work summarizes recent theoretical and technological advances and highlights an example of how RNNs helped to explain perplexing high-dimensional neurophysiological data in the prefrontal cortex.
Proceedings Article
Long short-term memory and Learning-to-learn in networks of spiking neurons
TL;DR: In this paper, the authors proposed LSNNs (Long Short-Term Memory) networks, which can be trained and configured by deep learning (BPTT combined with a rewiring algorithm that optimizes the network architecture).
Proceedings ArticleDOI
PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
TL;DR: This work presents a new family of modules, namely the PolyInception, which can be flexibly inserted in isolation or in a composition as replacements of different parts of a network, and demonstrates substantial improvements over the state-of-the-art on the ILSVRC 2012 benchmark.
Journal ArticleDOI
Mixed Neural Network Approach for Temporal Sleep Stage Classification
TL;DR: A comfortable configuration of a single-channel EEG on the forehead is found and it can be integrated with additional electrodes for simultaneous recording of the electro-oculogram, and use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.
References
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Journal ArticleDOI
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
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
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.