2 code implementations • NeurIPS 2023 • Marcello Massimo Negri, F. Arend Torres, Volker Roth
Studying conditional independence among many variables with few observations is a challenging task.
no code implementations • 26 May 2023 • F. Arend Torres, Marcello Massimo Negri, Marco Inversi, Jonathan Aellen, Volker Roth
We introduce Lagrangian Flow Networks (LFlows) for modeling fluid densities and velocities continuously in space and time.
no code implementations • 3 Jun 2022 • Fabricio Arend Torres, Marcello Massimo Negri, Monika Nagy-Huber, Maxim Samarin, Volker Roth
Physics-informed Neural Networks (PINNs) have recently emerged as a principled way to include prior physical knowledge in form of partial differential equations (PDEs) into neural networks.
no code implementations • pproximateinference AABI Symposium 2022 • Marcello Massimo Negri, Vincent Fortuin, Jan Stuehmer
Variational auto-encoders have proven to capture complicated data distributions and useful latent representations, while advances in meta-learning have made it possible to extract prior knowledge from data.