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Learning may need only a few bits of synaptic precision

, , , , and . (2016)cite arxiv:1602.04129Comment: 38 pages (main text: 16 pages), 5 figures; http://link.aps.org/doi/10.1103/PhysRevE.93.052313.
DOI: 10.1103/PhysRevE.93.052313

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Learning may need only a few bits of synaptic precision, , , , and . (2016)cite arxiv:1602.04129Comment: 38 pages (main text: 16 pages), 5 figures; http://link.aps.org/doi/10.1103/PhysRevE.93.052313.The Hidden-Manifold Hopfield Model and a learning phase transition., , , , and . CoRR, (2023)Entropic gradient descent algorithms and wide flat minima., , , , , , and . ICLR, OpenReview.net, (2021)A Scaling Hypothesis for the Euclidean Bipartite Matching Problem., , , and . CoRR, (2014)One-loop diagrams in the Random Euclidean Matching Problem., , and . CoRR, (2016)The Exponential Capacity of Dense Associative Memories., and . CoRR, (2023)Compressed sensing with l0-norm: statistical physics analysis and algorithms for signal recovery., , , , and . CoRR, (2023)Deep learning via message passing algorithms based on belief propagation., , , and . Mach. Learn. Sci. Technol., 3 (3): 35005 (2022)Compressed sensing with ℓ0-norm: statistical physics analysis & algorithms for signal recovery., , , , and . ITW, page 323-328. IEEE, (2023)On the role of synaptic stochasticity in training low-precision neural networks., , , , , , and . CoRR, (2017)