[Lisa_seminaires] SPECIAL TIME: UdeM-McGill-MITACS machine learning seminar Wed April 30th, 14:00, PAA3195

Hugo Larochelle larocheh at iro.umontreal.ca
Mer 23 Avr 19:36:11 EDT 2008


Next week's seminar (see http://www.iro.umontreal.ca/article.php3? 
id_article=107&lang=en):


Learning Deep Hierarchies of Sparse and Invariant Features


by Yann LeCun,
Courant Institute of Mathematical Science
New York University

Location: Pavillon André Aisenstadt (UdeM), room 3195
Time: April 30th 2008, 14:00

A long-term goal of Machine Learning research is to solve highy  
complex "intelligent" tasks, such as visual perception auditory  
perception, and language understanding. To reach that goal, the ML  
community must solve two problems: the Deep Learning Problem, and the  
Partition Function Problem.

There is considerable theoretical and empirical evidence that complex  
tasks, such as invariant object recognition in vision, require "deep"  
architectures, composed of multiple layers of trainable non-linear  
modules. The Deep Learning Problem is related to the difficulty of  
training such deep architectures.

Several methods have recently been proposed to train (or pre-train)  
deep architectures in an unsupervised fashion. Each layer of the deep  
architecture is composed of an encoder which computes a feature  
vector from the input, and a decoder which reconstructs the input  
from the features. A large number of such layers can be stacked and  
trained sequentially, thereby learning a deep hierarchy of features  
with increasing levels of abstraction. The training of each layer can  
be seen as shaping an energy landscape with low valleys around the  
training samples and high plateaus everywhere else. Forming these  
high plateaus constitute the so-called Partition Function problem.

A particular class of methods for deep energy-based unsupervised  
learning will be described that solves the Partition Function problem  
by imposing sparsity constraints on the features. The method can  
learn multiple levels of sparse and overcomplete representations of  
data. When applied to natural image patches, the method produces  
hierarchies of filters similar to those found in the mammalian visual  
cortex.

An application to category-level object recognition with invariance  
to pose and illumination will be described (with a live demo).  
Another application to vision-based navigation for off-road mobile  
robots will be described (with videos). The system autonomously  
learns to discriminate obstacles from traversable areas at long range.

This is joint work with Y-Lan Boureau, Sumit Chopra, Raia Hadsell, Fu- 
Jie Huang, Koray Kavakcuoglu, and Marc’Aurelio Ranzato.


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