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Poster
Learning to combine foveal glimpses with a third-order Boltzmann machine
Hugo Larochelle · Geoffrey E Hinton
We describe a model based on a Boltzmann machine with third-order connections that can learn how to accumulate information about a shape over several fixations. The model uses a retina that only has enough high resolution pixels to cover a small area of the image, so it must decide on a sequence of fixations and it must combine the "glimpse" at each fixation with the location of the fixation before integrating the information with information from other glimpses of the same object. We evaluate this model on a synthetic dataset and two image classification datasets, showing that it can perform at least as well as a model trained on whole images.
Author Information
Hugo Larochelle (Google DeepMind)
Geoffrey E Hinton (Google & University of Toronto)
Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978 and spent five years as a faculty member at Carnegie-Mellon where he pioneered back-propagation, Boltzmann machines and distributed representations of words. In 1987 he became a fellow of the Canadian Institute for Advanced Research and moved to the University of Toronto. In 1998 he founded the Gatsby Computational Neuroscience Unit at University College London, returning to the University of Toronto in 2001. His group at the University of Toronto then used deep learning to change the way speech recognition and object recognition are done. He currently splits his time between the University of Toronto and Google. In 2010 he received the NSERC Herzberg Gold Medal, Canada's top award in Science and Engineering.
Related Events (a corresponding poster, oral, or spotlight)
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2010 Oral: Learning to combine foveal glimpses with a third-order Boltzmann machine »
Thu. Dec 9th 05:50 -- 06:10 PM Room Regency Ballroom
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