Juin 2008 - Lisa_seminaires - mailman.iro.umontreal.ca

Upcoming Seminar - Stuart Russell, Berkeley: June 30/08
by Joelle Pineau 30 Jui '08

30 Jui '08

Hello everyone, Please note this important seminar. Stuart Russell is a world-renowned researcher in Artificial Intelligence, and author of the leading textbook on the topic. Everyone is encouraged to attend! Monday June 30th, 2008 McConnell Engineering Rm 12 2:00pm Hierarchical learning and decision making Stuart Russell Computer Science Division University of California, Berkeley Effective behaviour in complex environments requires some form of hierarchical structure. The first part of the talk covers hierarchical reinforcement learning, wherein guidance as to the correct hierarchical structure can be supplied to an agent in the form of a *partial program* in which choices may be left unspecified. The ALisp language - an partial programming extension of Lisp - includes reinforcement learning algorithms that converge to the optimal complete program consistent with the given partial program. ALisp includes constructs for specifying asynchronous concurrent activities. ALisp shows promise as a tool for scaling up reinforcement learning. The second part of the talk covers lookahead decision making with hierarchical constraints on behavior. Here the setting is classical hierarchical planning. We resolve a longstanding open problem by defining *angelic* semantics for high-level actions and providing the first algorithms capable of proving that a high-level plan is correct and optimal without considering its concrete implementations. We also consider online hierarchical search with bounded lookahead. [Joint work with Ron Parr, David Andre, Bhaskara Marthi, Andy Zimdars, David Latham, Carlos Guestrin, Jason Wolfe] Stuart Russell was born in Portsmouth, England. He received his B.A. with first-class honours in Physics from Wadham College, Oxford University in 1982, and his Ph.D. in Computer Science from Stanford in 1986. He then joined the faculty of the University of California, Berkeley, where he is currently Professor and Chair of Computer Science. In 2003 he was inducted as a Fellow of the Association for Computing Machinery. Stuart Russell is an author of the textbook Artificial Intelligence: A Modern Approach (together with Peter Norvig).

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UdeM-McGill-MITACS machine learning seminar: Thu June 26th, 14:00, AA-3195
by Dumitru Erhan 26 Jui '08

26 Jui '08

TODAY's UdeM-McGill-MITACS seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en) : Artificial Intelligence in Seven Years? by Léon Bottou NEC Labs America Location : Pavillon André-Aisenstadt (UdeM) room 3195 Date and Time : Thursday, June 26 2008, 14h00 This highly informal and highly prospective presentation discusses an ensemble of techniques which I believe form a plausible path to achieve artificial intelligence in the next decade. The discussion includes: available data and transfer learning, statistical learning theory is not yet ready to help, recursive representations in finite dimensional spaces, a general algorithm for parsing text and images, reasoning with neurons (artificial neurons !) Of course each of these points contain potential stumbling blocks. The purpose of this presentation is therefore to invite a discussion. P.S. - This is a whiteboard talk.

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UdeM-McGill-MITACS machine learning seminar: Thu June 26th, 14:00, AA-3195
by Dumitru Erhan 25 Jui '08

25 Jui '08

THURSDAY's UdeM-McGill-MITACS seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en) : Artificial Intelligence in Seven Years? by Léon Bottou NEC Labs America Location : Pavillon André-Aisenstadt (UdeM) room 3195 Date and Time : Thursday, June 26 2008, 14h00 This highly informal and highly prospective presentation discusses an ensemble of techniques which I believe form a plausible path to achieve artificial intelligence in the next decade. The discussion includes: available data and transfer learning, statistical learning theory is not yet ready to help, recursive representations in finite dimensional spaces, a general algorithm for parsing text and images, reasoning with neurons (artificial neurons !) Of course each of these points contain potential stumbling blocks. The purpose of this presentation is therefore to invite a discussion. P.S. - This is a whiteboard talk.

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REMINDER: UdeM-McGill-MITACS machine learning seminar: Fri June 20th, 15:30, AA-3195
by Dumitru Erhan 20 Jui '08

20 Jui '08

TOMORROW's UdeM-McGill-MITACS seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en) Learning hierarchical representations of natural images by Mike Lewicki, Computer Science Department and Center for the Neural Basis of Cognition Carnegie Mellon University Location : Pavillon André-Aisenstadt (UdeM) room 3195 Date and Time : June 20 2008, 15h30 What are the underlying computational principles that biology uses to transform the raw sensory signal into a hierarchy of representations that subserve higher-level perceptual tasks ? One hypothesis in this work is that biological representations are optimal from the viewpoint of statistical information processing, and adapt to the statistics of the natural sensory environment. In vision, we perceive complex natural edges, contours, and textures, whose retinal image is inherently highly variable. For accurate perception, the visual system must form abstract representations that remain invariant across individual fixations, but how this is achieved is poorly understood. In this talk, I will discuss an approach that is based on learning statistical distributions of local regions in a visual scene. The central hypothesis is learning these local distributions allows the visual system to generalize across similar images. I will present a model in which the joint activity of neurons encodes the probability distribution over their inputs and forms stable representations across complex patterns of variation. Trained on natural images, the model learns a compact set of functions that act as dictionary elements for image distributions typically encountered in natural scenes. Neurons in the model exhibit a wide range of properties observed in cortical neurons. These results provide a novel functional explanation for non-linear effects in complex cells in the primary visual cortex (V1) and make predictions about coding in higher visual areas, such as V2 and V4. This is joint work with Yan Karklin. Bio : Dr. Lewicki an associate professor in the Computer Science Department at Carnegie Mellon University and in the CMU-University of Pittsburgh Center for the Neural Basis of Cognition. He received his BS degree in mathematics and cognitive science from Carnegie Mellon University, his PhD degree in computation and neural systems from the California Institute of Technology, and did postdoctoral studies in the Computational Neurobiology Laboratory at the Salk Institute.

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UdeM-McGill-MITACS machine learning seminar: Fri June 20th, 15:30, AA-3195
by Dumitru Erhan 16 Jui '08

16 Jui '08

Next week's UdeM-McGill-MITACS seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en) Learning hierarchical representations of natural images by Mike Lewicki, Computer Science Department and Center for the Neural Basis of Cognition Carnegie Mellon University Location : Pavillon André-Aisenstadt (UdeM) room 3195 Date and Time : June 20 2008, 15h30 What are the underlying computational principles that biology uses to transform the raw sensory signal into a hierarchy of representations that subserve higher-level perceptual tasks ? One hypothesis in this work is that biological representations are optimal from the viewpoint of statistical information processing, and adapt to the statistics of the natural sensory environment. In vision, we perceive complex natural edges, contours, and textures, whose retinal image is inherently highly variable. For accurate perception, the visual system must form abstract representations that remain invariant across individual fixations, but how this is achieved is poorly understood. In this talk, I will discuss an approach that is based on learning statistical distributions of local regions in a visual scene. The central hypothesis is learning these local distributions allows the visual system to generalize across similar images. I will present a model in which the joint activity of neurons encodes the probability distribution over their inputs and forms stable representations across complex patterns of variation. Trained on natural images, the model learns a compact set of functions that act as dictionary elements for image distributions typically encountered in natural scenes. Neurons in the model exhibit a wide range of properties observed in cortical neurons. These results provide a novel functional explanation for non-linear effects in complex cells in the primary visual cortex (V1) and make predictions about coding in higher visual areas, such as V2 and V4. This is joint work with Yan Karklin. Bio : Dr. Lewicki an associate professor in the Computer Science Department at Carnegie Mellon University and in the CMU-University of Pittsburgh Center for the Neural Basis of Cognition. He received his BS degree in mathematics and cognitive science from Carnegie Mellon University, his PhD degree in computation and neural systems from the California Institute of Technology, and did postdoctoral studies in the Computational Neurobiology Laboratory at the Salk Institute.

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