4 papers
Universal Time Series Generation with Neural Controlled Differential Equations
Torben Berndt, Elyes Farjallah, Leif Seute +3
Recent work on the sequence universality of State Space Models (SSMs) has introduced efficient, maximally expressive continuous-time approaches for time-series modelling. While the…
Learning conformational ensembles of proteins based on backbone geometry
Nicolas Wolf, Leif Seute, Vsevolod Viliuga +3
Deep generative models have recently been proposed for sampling protein conformations from the Boltzmann distribution, as an alternative to often prohibitively expensive Molecular…
Flexibility-Conditioned Protein Structure Design with Flow Matching
Vsevolod Viliuga, Leif Seute, Nicolas Wolf +4
Recent advances in geometric deep learning and generative modeling have enabled the design of novel proteins with a wide range of desired properties. However, current state-of-the-…
Generating Highly Designable Proteins with Geometric Algebra Flow Matching
Simon Wagner, Leif Seute, Vsevolod Viliuga +3
We introduce a generative model for protein backbone design utilizing geometric products and higher order message passing. In particular, we propose Clifford Frame Attention (CFA),…