Journal ArticleDOI
Learning representations by back-propagating errors
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TLDR
Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.Abstract:
We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.read more
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Journal ArticleDOI
Convolutional Recurrent Deep Learning Model for Sentence Classification
Abdalraouf Hassan,Ausif Mahmood +1 more
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Book ChapterDOI
Connectionist Temporal Modeling for Weakly Supervised Action Labeling
TL;DR: In this paper, the Extended Connectionist Temporal Classification (ECTC) framework is proposed to evaluate all possible alignments via dynamic programming and explicitly enforce their consistency with frame-to-frame visual similarities.
Journal ArticleDOI
Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts
TL;DR: Results demonstrated that the proposed DM-LSTM model incorporated with three deep learning algorithms could significantly improve the spatio-temporal stability and accuracy of regional multi-step-ahead air quality forecasts.
Journal ArticleDOI
Training feedforward neural networks using multi-verse optimizer for binary classification problems
TL;DR: The comparative study demonstrates that MVO is very competitive and outperforms other training algorithms in the majority of datasets in terms of improved local optima avoidance and convergence speed.
Posted Content
Optimization Methods for Large-Scale Machine Learning
TL;DR: A major theme of this study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient method has traditionally played a central role while conventional gradient-based nonlinear optimization techniques typically falter, leading to a discussion about the next generation of optimization methods for large- scale machine learning.