TL;DR
Inspired by rigorous formal analogies between stochastic neural networks and lattice models in which molecules occupy discrete sites, contribute species-dependent interaction energies with neighbors.
It is argued that the inductive bias of locality means that the machinery of effective field theory from physics can be usefully applied to describe the denoising dynamics of Brownian motion.
The Anderson tight-binding model is a fundamental model of quantum transport and localization in disordered media. Completing a problem left open by Bourgain and Kenig, this paper proves Anderson localization near the bottom of the spectrum for the lattice Anderson model with Bernoulli potential, in any dimension $d\ge...
We study a one-dimensional self-consistent transfer operator generated by a repulsive mean-field coupling. The interaction admits a natural interpretation as a myopic relocation dynamics balancing congestion and uniformly distributed resources. Using the quantile representation of probability measures, combined with se...
Kinetically constrained lattice gases are interacting particle systems with conserved number of particles, having degenerate rates caused by a kinetic constraint. Over the past decade, there has been major progress in the study of their non-conservative counterpart, kinetically constrained spin models: in dimensions 1...
We consider the problem of recovering the community structure in the stochastic block model. We aim to describe the mutual information between the observed network and the actual community structure as the number of nodes diverges while the average degree of a given node remains bounded. Our main contribution is a repr...
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