2 citations · 4 across the 6 of their papers we have counts for
6 papers
Marginal Flow: a flexible and efficient framework for density estimation
Marcello Massimo Negri, Jonathan Aellen, Manuel Jahn +2
Current density modeling approaches suffer from at least one of the following shortcomings: expensive training, slow inference, approximate likelihood, mode collapse or architectur…
NIMO: a Nonlinear Interpretable MOdel
Shijian Xu, Marcello Massimo Negri, Volker Roth
Deep learning has achieved remarkable success across many domains, but it has also created a growing demand for interpretability in model predictions. Although many explainable mac…
Injective flows for star-like manifolds
Marcello Massimo Negri, Jonathan Aellen, Volker Roth
Normalizing Flows (NFs) are powerful and efficient models for density estimation. When modeling densities on manifolds, NFs can be generalized to injective flows but the Jacobian d…
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…