[Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar: Fri June 20th, 15:30, AA-3195
Dumitru Erhan
erhandum at IRO.UMontreal.CA
Lun 16 Juin 10:59:57 EDT 2008
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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