1 citations · 2 across the 6 of their papers we have counts for
6 papers
Diffeomorphic Optimization
Ludwig Winkler, Andrew Leaver-Fay, Joseph Kleinhenz +1
Generative models learn data distributions that reside on a low-dimensional manifold within a higher-dimensional ambient space. Optimizing differentiable objectives on this manifol…
Do we need equivariant models for molecule generation?
Ewa M. Nowara, Joshua Rackers, Patricia Suriana +4
Deep generative models are increasingly used for molecular discovery, with most recent approaches relying on equivariant graph neural networks (GNNs) under the assumption that expl…
Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins
Frédéric A. Dreyer, Jan Ludwiczak, Karolis Martinkus +8
We introduce Ibex, a pan-immunoglobulin structure prediction model that achieves state-of-the-art accuracy in modeling the variable domains of antibodies, nanobodies, and T-cell re…
Equivariant Neural Tangent Kernels
Philipp Misof, Pan Kessel, Jan E. Gerken
Little is known about the training dynamics of equivariant neural networks, in particular how it compares to data augmented training of their non-equivariant counterparts. Recently…
Fast and Unified Path Gradient Estimators for Normalizing Flows
Lorenz Vaitl, Ludwig Winkler, Lorenz Richter +1
Recent work shows that path gradient estimators for normalizing flows have lower variance compared to standard estimators for variational inference, resulting in improved training.…
Emergent Equivariance in Deep Ensembles
Jan E. Gerken, Pan Kessel
We show that deep ensembles become equivariant for all inputs and at all training times by simply using data augmentation. Crucially, equivariance holds off-manifold and for any ar…