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The Next Generation of Neural Networks

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  • Description: Google Tech Talks
    November, 29 2007

    In the 1980's, new learning algorithms for neural networks promised to
    solve difficult classification tasks, like speech or object recognition,
    by learning many layers of non-linear features. The results were
    disappointing for two reasons: There was never enough labeled data to
    learn millions of complicated features and the learning was much too slow
    in deep neural networks with many layers of features. These problems can
    now be overcome by learning one layer of features at a time and by
    changing the goal of learning. Instead of trying to predict the labels,
    the learning algorithm tries to create a generative model that produces
    data which looks just like the unlabeled training data. These new neural
    networks outperform other machine learning methods when labeled data is
    scarce but unlabeled data is plentiful. An application to very fast
    document retrieval will be described.

    Speaker: Geoffrey Hinton
    Geoffrey Hinton received his BA in experimental psychology from Cambridge in
    1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He did
    postdoctoral work at Sussex University and the University of California San
    Diego and spent five years as a faculty member in the Computer Science
    department at Carnegie-Mellon University. He then became a fellow of the
    Canadian Institute for Advanced Research and moved to the Department of
    Computer Science at the University of Toronto. He spent three years from 1998
    until 2001 setting up the Gatsby Computational Neuroscience Unit at University
    College London and then returned to the University of Toronto where he is a
    University Professor. He holds a Canada Research Chair in Machine Learning. He
    is the director of the program on "Neural Computation and Adaptive Perception"
    which is funded by the Canadian Institute for Advanced Research.

    Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada,
    and the Association for the Advancement of Artificial Intelligence. He is an
    honorary foreign member of the American Academy of Arts and Sciences, and a
    former president of the Cognitive Science Society. He received an honorary
    doctorate from the University of Edinburgh in 2001. He was awarded the first
    David E. Rumelhart prize (2001), the IJCAI award for research excellence
    (2005), the IEEE Neural Network Pioneer award (1998) and the ITAC/NSERC award
    for contributions to information technology (1992).

    A simple introduction to Geoffrey Hinton's research can be found in his
    articles in Scientific American in September 1992 and October 1993. He
    investigates ways of using neural networks for learning, memory, perception and
    symbol processing and has over 200 publications in these areas. He was one of
    the researchers who introduced the back-propagation algorithm that has been
    widely used for practical applications. His other contributions to neural
    network research include Boltzmann machines, distributed representations,
    time-delay neural nets, mixtures of experts, Helmholtz machines and products of
    experts. His current main interest is in unsupervised learning procedures
    for neural networks with rich sensory input.
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