152 citations · 465 across the 17 of their papers we have counts for
5 papers · 1 filter
Smooth Normalizing Flows
Jonas Köhler, Andreas Krämer, Frank Noé
Normalizing flows are a promising tool for modeling probability distributions in physical systems. While state-of-the-art flows accurately approximate distributions and energies, a…
Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces
Ziyad Sheebaelhamd, Konstantinos Zisis, Athina Nisioti +3
Multi-agent control problems constitute an interesting area of application for deep reinforcement learning models with continuous action spaces. Such real-world applications, howev…
Generating stable molecules using imitation and reinforcement learning
Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen +3
Chemical space is routinely explored by machine learning methods to discover interesting molecules, before time-consuming experimental synthesizing is attempted. However, these met…
Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks
Antonio Orvieto, Jonas Kohler, Dario Pavllo +2
This paper revisits the so-called vanishing gradient phenomenon, which commonly occurs in deep randomly initialized neural networks. Leveraging an in-depth analysis of neural chain…
Learning Generative Models of Textured 3D Meshes from Real-World Images
Dario Pavllo, Jonas Kohler, Thomas Hofmann +1
Recent advances in differentiable rendering have sparked an interest in learning generative models of textured 3D meshes from image collections. These models natively disentangle p…