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Vincent Stimper
Empirical Inference Doctoral Researcher Alumni
I was a PhD student in the ELLIS program being supervised by Dr. Jose Miguel Hernandez Lobato (University of Cambridge) and Prof. Bernhard Schölkopf (Max Planck Institute for Intelligent Systems). During my PhD, I worked on probabilistic modeling and representation learning with a focus on normalizing flows and applications to the physical sciences. My mission is to create theoretically substantiated machine learning algorithms for natural science and engineering applications.
For more recent updates, please visit my personal website.
Coupling normalizing flows allow for fast sampling and density evaluation, making them the tool of choice for probabilistic modeling of physical systems. However, the standard coupling architecture precludes endowing flows that operate on the Cartesian coordinates of atoms with the SE(3) and permutation invariances of physical systems. This work proposes a coupling flow that preserves SE(3) and permutation equivariance by performing coordinate splits along additional augmented dimensions. At each layer, the flow maps atoms' positions into learned SE(3) invariant bases, where we apply standard flow transformations, such as monotonic rational-quadratic splines, before returning to the original basis. Crucially, our flow preserves fast sampling and density evaluation, and may be used to produce unbiased estimates of expectations with respect to the target distribution via importance sampling.
Concurrently with Klein et al. [2023], we are the first to learn the full Boltzmann distribution of alanine dipeptide by only modeling the Cartesian positions of its atoms.
The article is published in NeurIPS 2023, and the code is publicly available on GitHub.