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oaioai:dspace.mit.edu:1721.1/100194

On Invariance and Selectivity in Representation Learning

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Publication date
23 March 2015
Publisher
Center for Brains, Minds and Machines (CBMM), arXiv

Abstract

We discuss data representation which can be learned automatically from data, are invariant to transformations, and at the same time selective, in the sense that two points have the same representation only if they are one the transformation of the other. The mathematical results here sharpen some of the key claims of i-theory, a recent theory of feedforward processing in sensory cortex.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216

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This paper was published in DSpace@MIT.

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