On Fri, Oct 1, 2010 at 10:06, Dumitru Erhan <erhandum@iro.umontreal.ca> wrote:

The UdeM-McGill-MITACS machine learning seminar series is back with an
exciting fall schedule. Next week's seminar (see
http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en):

Learning Spatial and Transformational Invariants for Visual Representation

by Charles Cadieu
Redwood Center for Theoretical Neuroscience
University of California, Berkeley

Location: Pavillon André-Aisenstadt (UdeM), room AA-3195
Time: Thursday, October 7, 14:00

Abstract: Learning abstract, invariant properties of the visual word
is a key attribute of biological vision systems. I will describe a
hierarchical, probabilistic model that learns to extract invariant
spatial structure and invariant motion structure from movies of the
natural environment. The first layer in the model produces a sparse
decomposition of local edge and motion structure. This decomposition
is achieved through a complex-valued sparse coding model in which
amplitudes represent the presence of edge structure at specific
positions, orientations, and spatial scales, and phases represent the
precise positions of edges and how image structure changes through
time. This decomposition into amplitude and phase exposes statistical
dependencies that the top layer in the model captures as two types of
invariances: spatial and transformational. The spatial invariants are
a sparse code of the patterns in the first layer amplitude components.
They code a rich set of multi-scale edges, textures, and texture
defined edge boundaries. The transformational invariants are a sparse
representation of patterns of change in the first layer phase
components. They learn a multi-scale code of motion, spanning local
and global motions, and are capable of learning complex motions such
as zooming, rotation, and deformation. Besides extracting abstract,
invariant properties of the visual world, I will show how the
hierarchical model provides a concrete model of cortical feedback that
is useful for perception under noisy or ambiguous conditions.