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01409 am a22002173u 4500 |
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112284 |
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|a dc
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|a Anselmi, Fabio
|e author
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|a Center for Brains, Minds, and Machines
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|a Anselmi, Fabio
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|a Rosasco, Lorenzo
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|a Poggio, Tomaso A
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|a Rosasco, Lorenzo
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|a Poggio, Tomaso A
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|a On invariance and selectivity in representation learning
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|b Oxford University Press (OUP),
|c 2017-11-27T14:51:43Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/112284
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|a 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).
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|a National Science Foundation (U.S.) (Award CCF-1231216)
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|a Article
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|t Information and Inference
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