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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Fusion of face and speech data for person identity verification
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Understanding adversarial training: Increasing local stability of supervised models through robust optimization
TL;DR: The proposed framework generalizes adversarial training, as well as previous approaches for increasing local stability of ANNs, and increases the robustness of the network to existing adversarial examples, while making it harder to generate new ones.
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Does the nervous system depend on kinesthetic information to control natural limb movements
Simon C. Gandevia,David Burke +1 more
TL;DR: In this paper, the authors draw together two groups of experimental studies on the control of human movement through peripheral feedback and centrally generated signals of motor commands, concluding that subjects can perceive their motor commands under various conditions, but that this is inadequate for normal movement.