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From Associative Memories to Deep Networks

Author(s)
Poggio, Tomaso
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Abstract
About fifty years ago, holography was proposed as a model of associative memory. Associative memories with similar properties were soon after implemented as simple networks of threshold neurons by Willshaw and Longuet-Higgins. In these pages I will show that today’s deep nets are an incremental improvement of the original associative networks. Thinking about deep learning in terms of associative networks provides a more realistic and sober perspective on the promises of deep learning and on its role in eventually understanding human intelligence. As a bonus, this discussion also uncovers connections with several interesting topics in applied math: random features, random projections, neural ensembles, randomized kernels, memory and generalization, vector quantization and hierarchical vector quantization, random vectors and orthogonal basis, NTK and radial kernels.
Date issued
2021-01-12
URI
https://hdl.handle.net/1721.1/129402
Publisher
Center for Brains, Minds and Machines (CBMM)
Series/Report no.
CBMM Memo;114

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