[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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