[Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar Thu Oct. 7 at 14h00, AA-3195

Dumitru Erhan erhandum at iro.umontreal.ca
Mer 6 Oct 14:46:47 EDT 2010


This is a reminder for tomorrow's talk!

On Fri, Oct 1, 2010 at 10:06, Dumitru Erhan <erhandum at 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.
>
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