Inference and learning in sparse systems with multiple states
Journal Article
·
· Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics (Print)
- Human Genetics Foundation, Via Nizza 52, I-10126 Torino (Italy)
- Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129 Torino (Italy)
We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a 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.
- OSTI ID:
- 21560287
- Journal Information:
- Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics (Print), Vol. 83, Issue 5; Other Information: DOI: 10.1103/PhysRevE.83.056114; (c) 2011 American Institute of Physics; ISSN 1539-3755
- Country of Publication:
- United States
- Language:
- English
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