Keynote Title: The Computational Magic of Pattern Recognition in Cortex: A Theory of Selectivity and Invariance
Keynote Lecturer: Dr. Tomaso Poggio
Keynote Chair: Dr. Mário Figueiredo
Presented on: 17-02-2013
Abstract: I conjecture that the sample complexity of object recognition is mostly due to geometric image transformations and that a main goal of the ventral stream is to learn-and-discount image transformations while preserving sufficient selectivity. The theory predicts that the size of the receptive fields determines which transformations are learned during development; that the transformation represented in each area determines the tuning of the neurons in the area; and that class-specific transformations are learned and represented at the top of the ventral stream hierarchy. In problems of pattern recognition, hierarchical, layered architectures -- similar to cortex -- may exploit in an optimal way unsupervised learning of transformations to provide invariant and discriminative signatures to a supervised classifier.
Presented at the following Conference: ICPRAM, International Conference on Pattern Recognition Applications and Methods
Conference Website: icpram.org
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