79 citations · 174 across the 7 of their papers we have counts for
3 papers · 1 filter
Rigid Body Flows for Sampling Molecular Crystal Structures
Jonas Köhler, Michele Invernizzi, Pim de Haan +1
Normalizing flows (NF) are a class of powerful generative models that have gained popularity in recent years due to their ability to model complex distributions with high flexibili…
Escaping Saddles with Stochastic Gradients
Hadi Daneshmand, Jonas Kohler, Aurelien Lucchi +1
We analyze the variance of stochastic gradients along negative curvature directions in certain non-convex machine learning models and show that stochastic gradients exhibit a stron…
Sub-sampled Cubic Regularization for Non-convex Optimization
Jonas Moritz Kohler, Aurelien Lucchi
We consider the minimization of non-convex functions that typically arise in machine learning. Specifically, we focus our attention on a variant of trust region methods known as cu…