Inference and learning in sparse systems with multiple states

Details Inference and learning in sparse systems with multiple states Inference and learning in sparse systems with multiple states

2011-04-14

arxiv.org/abs/1104.2775v1

Phys. Rev. E 83, 056114 (2011)

Physics

Condensed Matter

Disordered Systems and Neural Networks

15 pages, 10 figures, to be published in Phys. Rev. E

Scientific paper

We discuss how inference can be performed when data are sampled from the non-ergodic phase of systems with multiple attractors. We take as model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin glass phase where all memories are lost.

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