Modern generative machine learning models are able to create realistic outputs far beyond
their training data, such as photorealistic artwork, accurate protein structures or …

We investigate the stationary (late-time) training regime of single-and two-layer
underparameterized linear neural networks within the continuum limit of stochastic gradient …

We have formulated a family of machine learning problems as the time evolution of
parametric probabilistic models (PPMs), inherently rendering a thermodynamic process. Our …

We prove that nested canalizing functions are the minimum-sensitivity Boolean functions for
any activity ratio and we determine the functional form of this boundary which has a …

Most iterative neural network training methods use estimates of the loss function over small
random subsets (or minibatches) of the data to update the parameters, which aid in …

The dynamical evolution of a neural network during training has been an incredibly
fascinating subject of study. First principal derivation of generic evolution of variables in …