3 papers
cs.LG2026
AlphaFold's Bayesian Roots in Probability Kinematics
Thomas Hamelryck, Kanti V. Mardia
The seminal breakthrough of AlphaFold in protein structure prediction relied on a learned potential energy function parameterized by deep models, in contrast to its successors Alph…
cs.LG2026
Compact Circulant Layers with Spectral Priors
Joseph Margaryan, Thomas Hamelryck
Critical applications in areas such as medicine, robotics and autonomous systems require compact (i.e., memory efficient), uncertainty-aware neural networks suitable for edge and o…
cs.LG2025
ELBOing Stein: Variational Bayes with Stein Mixture Inference
Ola Rønning, Eric Nalisnick, Christophe Ley +2
Stein variational gradient descent (SVGD) [Liu and Wang, 2016] performs approximate Bayesian inference by representing the posterior with a set of particles. However, SVGD suffers…