11 citations · 40 across the 10 of their papers we have counts for
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cs.LG2024★ 1 cited
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows
Bálint Máté, François Fleuret
We consider the problem of sampling lattice field configurations on a lattice from the Boltzmann distribution corresponding to some action. Since such densities arise as approximat…
cs.LG2023★ 4 cited
Graph Neural Networks Go Forward-Forward
Daniele Paliotta, Mathieu Alain, Bálint Máté +1
We present the Graph Forward-Forward (GFF) algorithm, an extension of the Forward-Forward procedure to graphs, able to handle features distributed over a graph's nodes. This allows…
cs.LG2022★ 1 cited
Flowification: Everything is a Normalizing Flow
Bálint Máté, Samuel Klein, Tobias Golling +1
The two key characteristics of a normalizing flow is that it is invertible (in particular, dimension preserving) and that it monitors the amount by which it changes the likelihood…