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cs.LG2026
Sample-efficient learning with Dense Associative Memories
Andrew Lucas, Dmitry Krotov
We analytically study the evolution of the energy function of Dense Associative Memories (DenseAMs) as they train on randomly sampled codewords of random expander low-density parit…
cs.LG2026
Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
Arthur N. Montanari, Francesco Bullo, Dmitry Krotov +1
Recent advances at the intersection of control theory, neuroscience, and machine learning have revealed novel mechanisms by which dynamical systems perform computation. These advan…