On invariance and selectivity in representation learning
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Author(s)
Anselmi, Fabio
Rosasco, Lorenzo
Poggio, Tomaso A
Date Issued
May 2016
Journal
Information and Inference
Publisher
Oxford University Press (OUP)
Citation
Anselmi, Fabio et al. “On Invariance and Selectivity in Representation Learning.” Information and Inference 5, 2 (May 2016): 134–158 © 2016 The Author(s)
Version
Original manuscript
Abstract
We study the problem of learning from data representations that are invariant to transformations, and at the same time selective, in the sense that two points have the same representation if one is 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 (Anselmi et al., 2013, Theor. Comput. Sci. and arXiv:1311.4158; Anselmi et al., 2013, Magic materials: a theory of deep hierarchical architectures for learning sensory representations. CBCL Paper; Anselmi & Poggio, 2010, Representation learning in sensory cortex: a theory. CBMM Memo No. 26).
MIT Department
Center for Brains, Minds, and Machines
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1093/IMAIAI/IAW009
