Next week's seminar (see http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en):
Evaluating and Learning Invariant
Features
by Ian Goodfellow
Stanford University
Location:
Pavillon André-Aisenstadt (UdeM),
room AA-3195
Time: Thursday, March 4, 14:30.
Abstract:
One approach to unsupervised learning is to attempt to
discover a function that decomposes the input into variables
representing distinct factors of variation. A weaker version of this is
to learn a representation where each variable is invariant to a small
number of confounding aspects of the input. Experimental results show
that the current degree of invariance achieved by some deep learning
algorithms is small compared to that achieved by systems hand-designed
to be invariant to specific transformations. I discuss some ideas for
future invariance evaluation experiments and for methods of
automatically learning to separate factors of variation.
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