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Pseudo-likelihood produces associative memories able to generalize, even for asymmetric couplings
Francesco D'Amico, Dario Bocchi, Luca Maria Del Bono +2
Energy-based probabilistic models learned by maximizing the likelihood of the data are limited by the intractability of the partition function. A widely used workaround is to maxim…
Statistical mechanics of vector Hopfield network near and above saturation
Flavio Nicoletti, Francesco D'Amico, Matteo Negri
We study analytically and numerically a Hopfield fully-connected network with -dimensional vector spins. These networks are models of associative memory that generalize the stan…
Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
Francesco D'Amico, Dario Bocchi, Matteo Negri
Scaling laws in deep learning -- empirical power-law relationships linking model performance to resource growth -- have emerged as simple yet striking regularities across architect…