The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
The space of interactions in neural network models
Open accessAbstract
The typical fraction of the space of interactions between each pair of N Ising spins which solve the problem of storing a given set of p random patterns as N-bit spin configurations is considered. The volume is calculated explicitly as a function of the storage ratio, alpha =p/N, of the value kappa (>0) of the product of the spin and the magnetic field at each site and of the magnetisation, m. Here m may vary between 0 (no correlation) and 1 (completely correlated). The capacity increases with the correlation between patterns from alpha =2 for correlated patterns with kappa =0 and tends to infinity as m tends to 1. The calculations use a saddle-point method and the order parameters at the saddle point are assumed to be replica symmetric. This solution is shown to be locally stable. A local iterative learning algorithm for updating the interactions is given which will converge to a solution of given kappa provided such solutions exist.
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Cites
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Machine Learning for Fluid Mechanics
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Metadata
- Authors
- Gardner, E.
- Venue
- Journal of Physics A: Mathematical and General
- Publisher
- IOP Publishing
- Published
- 1988-01-07
- Volume
- 21
- Issue
- 1
- Pages
- 257-270
- ISSN
- 0305-4470 (ISSN-L)
- 1361-6447
- OpenAlex
- W2030450972
- Cited by
- 1,047
- References
- 25
- FWCI
- 57.57
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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