2011/05/31 by Haiping Huang, Haijun Zhou
Computer Science · Engineering · Mathematics · Neuroscience · Physics and Astronomy · #Advanced Memory and Neural Computing #Algorithm #Artificial intelligence #Artificial neural network #Binary number #Computation #Computer science #Energy (signal processing) #Hamming code #Hamming distance #Hamming space #Hamming weight #Mathematical optimization #Mathematics #Neural Networks and Applications #Neural dynamics and brain function #Pattern recognition (psychology) #Set (abstract data type) #Space (punctuation) #Statistics #Synaptic weight #Theoretical computer science #cond-mat.dis-nn #cond-mat.stat-mech #cs.IT #math.IT
paper · pdf · doi:10.1209/0295-5075/96/58003
published as EPL, 96 (2011) 58003 · 7 pages, 4 figures, figures and references added
arxiv created 2011/07/11 · openalex publication_date 2011/11/16 · arxiv updated 2011/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Learning in networks of binary synapses is known to be an NP-complete problem. A combined stochastic local search strategy in the synaptic weight space is constructed to further improve the learning performance of a single random walker. We apply two correlated random walkers guided by their Hamming distance and associated energy costs (the number of unlearned patterns) to learn a same large set of patterns. Each walker first learns a small part of the whole pattern set (partially different for both walkers but with the same amount of patterns) and then both walkers explore their respective weight spaces cooperatively to find a solution to classify the whole pattern set correctly. The desired solutions locate at the common parts of weight spaces explored by these two walkers. The efficiency of this combined strategy is supported by our extensive numerical simulations and the typical Hamming distance as well as energy cost is estimated by an annealed computation.