#geoffreyhinton#deepbeliefnetworks#restrictedboltzmannmachines
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84K views16 years ago3 recommendations
AI and machine learningNeural networks and deep learningClassic machine learning
Geoffrey Hinton explains how to efficiently learn deep networks from unlabeled data and introduces multiplicative interactions into basic learning modules.
Google Tech Talk March 19, 2010 ABSTRACT Presented by Geoff Hinton, University of Toronto. Deep networks can be learned efficiently from unlabeled data. The layers of representation are learned one at a time using a simple learning module that has only one layer of latent variables. The values of the latent variables of one module form the data for training the next module. Although deep networks have been quite successful for tasks such as object recognition, information retrieval, and modeling motion capture data, the simple learning modules do not have multiplicative interactions which are very useful for some types of data. The talk will show how to introduce multiplicative interactions into the basic learning module in a way that preserves the simple rules for learning and perceptual inference. The new module has a structure that is very similar to the simple cell/complex cell hierarchy that is found in visual cortex. The multiplicative interactions are useful for modeling images, image transformations, and different styles of human walking. Speaker bio: cs.toronto.edu/~hinton/bio.htmlWhat people say. Highly recommended as an excellent, slightly technical introduction to deep belief networks and unsupervised learning by a pioneer of the field.
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