2 citations · 3 across the 4 of their papers we have counts for
4 papers
Conditional Matrix Flows for Gaussian Graphical Models
Marcello Massimo Negri, F. Arend Torres, Volker Roth
Studying conditional independence among many variables with few observations is a challenging task. Gaussian Graphical Models (GGMs) tackle this problem by encouraging sparsity in…
Lagrangian Flow Networks for Conservation Laws
F. Arend Torres, Marcello Massimo Negri, Marco Inversi +2
We introduce Lagrangian Flow Networks (LFlows) for modeling fluid densities and velocities continuously in space and time. By construction, the proposed LFlows satisfy the continui…
Mesh-free Eulerian Physics-Informed Neural Networks
Fabricio Arend Torres, Marcello Massimo Negri, Monika Nagy-Huber +2
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…
Learning Invariances with Generalised Input-Convex Neural Networks
Vitali Nesterov, Fabricio Arend Torres, Monika Nagy-Huber +2
Considering smooth mappings from input vectors to continuous targets, our goal is to characterise subspaces of the input domain, which are invariant under such mappings. Thus, we w…