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视频:Hinton在Cambridge介绍深度学习及其最新进展的报告

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Description: Deep Learning, Professor Geoffrey Hinton, FRS, University of Toronto and Google

Created: 2015-06-29 14:45
Collection: Division F Talks
Publisher: University of Cambridge
Copyright: Geoffrey Hinton
Language: eng (English)

Abstract: I will describe an efficient, unsupervised learning procedure for a simple type of two-layer neural network called a Restricted Boltzmann Machine. I will then show how this algorithm can be used recursively to learn multiple layers of features without requiring any supervision. After this unsupervised “pre-training”, the features in all layers can be fine-tuned to be better at discriminating between classes by using the standard backpropagation procedure from the 1980s. Unsupervised pre-training greatly improves generalization to new data, especially when the number of labelled examples is small. Ten years ago, the pre-training approach initiated a revival of research on deep, feedforward neural networks. I will describe some of the major successes of deep networks for speech recognition, object recognition and machine translation and I will speculate about where this research is headed. The fact that backpropagation learning is now the method of choice for a wide variety of really difficult tasks means that neuroscientists may need to reconsider their well-worn arguments about why it cannot possibly be occurring in cortex. I shall conclude by undermining two of the commonest objections to the idea that cortex is actually backpropagating error derivatives through a hierarchy of cortical areas and I shall show that spike-time dependent plasticity is a signature of backpropagation.

传送门:http://sms.cam.ac.uk/media/2017973?format=mpeg4&quality=360p

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