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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Journal ArticleDOI
Going deeper with two-stream ConvNets for action recognition in video surveillance
TL;DR: A novel deeper two-stream ConvNets has been designed for the learning of action complexity and with a dis-order strategy of training/testing video sets, the proposed model and learning strategy are able to collaboratively achieve a significant improvement of action recognition.
Journal ArticleDOI
Very short-term forecasting of wind power generation using hybrid deep learning model
TL;DR: A novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora wind farm located in New South Wales, Australia and exhibits superior performance in both the data sets over other forecasting models.
Proceedings Article
A Probabilistic Model for Chord Progressions
TL;DR: A distributed representation for chords is designed such that Euclidean distances roughly correspond to psychoacoustic dissimilarities and Estimated probabilities of chord substitutions are derived and are used to introduce smoothing in graphical models observing chord progressions.
Book ChapterDOI
Real-Time Sign Language Gesture (Word) Recognition from Video Sequences Using CNN and RNN
TL;DR: This work intends to take a basic step in bridging this communication gap using Sign Language Recognition by using inception model which is a deep convolutional neural network (CNN) and RNN to train the model on temporal features.
Neural Tree Indexers for Text Understanding.
Tsendsuren Munkhdalai,Hong Yu +1 more
TL;DR: Neural Tree Indexers (NTI) as discussed by the authors constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion, which can then be applied to both structure and node function.
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.