Quasi-Newton (QN) methods have long been among the most effective methods for large-scale unconstrained convex optimization. Two obstacles have limited their use in deep learning: non-convexity and enormous parameter sizes. We introduce …
We show how to estimate a generative model from samples by parameterizing its score function as the electric field of a static charge distribution. The score function is given by the gradient of the model's log-density, and as such, it must define a …
Empirical Bayes (EB) performs simultaneous inference across many related latent variables. Classical EB assumes that the likelihood $p(x | z)$ is tractable. In many scientific applications, however, the likelihood is available only through a …
Meta-learning leverages related source tasks to learn an initialization that can be quickly fine-tuned to a target task with limited labeled examples. However, many popular meta-learning algorithms, such as model-agnostic meta-learning (MAML), only …