'Visible units' diaporamas de présentation

Deep Learning: An Overview

Deep Learning: An Overview. Weifeng Li, Victor Benjamin, Xiao Liu, and Hsinchun Chen University of Arizona. Acknowledgements. Many of the pictures, results, and other materials are taken from: Aarti Singh, Carnegie Mellon University Andrew Ng, Stanford University

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Ch12: Boltzmann Machine

Ch12: Boltzmann Machine. 12.1 Introduction. ? The major difference between BM and traditional NN is that the output function of PEs characterized by. BM: a nondeterministic function characterized by a stochastic function of inputs.

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CSC2535 Lecture 4 Boltzmann Machines, Sigmoid Belief Nets and Gibbs sampling

CSC2535 Lecture 4 Boltzmann Machines, Sigmoid Belief Nets and Gibbs sampling. Geoffrey Hinton. Instead of using the net to store memories, use it to construct interpretations of sensory input. The input is represented by the visible units.

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Geoffrey Hinton

CSC321: Tutorial with extra material on: Semantic Hashing and Conditional Restricted Boltzmann Machines (This will not be in the final exam). Geoffrey Hinton. Finding binary codes for documents. 2000 reconstructed counts. Train an auto-encoder using 30 logistic units for the code layer.

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Geoffrey Hinton University of Toronto

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Geoffrey Hinton University of Toronto

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Geoffrey Hinton University of Toronto

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Emerging Directions in Statistical Modeling in Speech Recognition

Emerging Directions in Statistical Modeling in Speech Recognition. Joseph Picone and Amir Harati Institute for Signal and Information Processing Temple University Philadelphia, Pennsylvania, USA. Abstract.

115 views • 11 slides