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
Handwritten digit recognition: benchmarking of state-of-the-art techniques
TL;DR: The results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques are competitive to the best ones previously reported on the same databases.
Journal ArticleDOI
Neuromorphic computing with multi-memristive synapses
Irem Boybat,Irem Boybat,Manuel Le Gallo,S. R. Nandakumar,S. R. Nandakumar,Timoleon Moraitis,Thomas Parnell,Tomas Tuma,Bipin Rajendran,Yusuf Leblebici,Abu Sebastian,Evangelos Eleftheriou +11 more
TL;DR: A multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme to address challenges associated with the non-ideal memristive device behavior is proposed.
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Fast Perceptual Learning in Visual Hyperacuity
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Journal ArticleDOI
Deep Learning in Drug Discovery.
TL;DR: An overview of this emerging field of molecular informatics, the basic concepts of prominent deep learning methods are presented, and motivation to explore these techniques for their usefulness in computer‐assisted drug discovery and design is offered.
Journal ArticleDOI
Deep Learning for Single Image Super-Resolution: A Brief Review
TL;DR: Recently, powerful deep learning algorithms have been applied to SISR and have achieved state-of-the-art performance as discussed by the authors, which is a notoriously challenging ill-posed problem that aims to obtain a high resolution output from one of its low-resolution versions.