11 citations · 40 across the 10 of their papers we have counts for
10 papers
Enhancing Diffusion-Based Sampling with Molecular Collective Variables
Juno Nam, Bálint Máté, Artur P. Toshev +6
Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for mole…
Accurate and scalable exchange-correlation with deep learning
Giulia Luise, Chin-Wei Huang, Thijs Vogels +25
Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable…
Solvation Free Energies from Neural Thermodynamic Integration
Bálint Máté, François Fleuret, Tristan Bereau
We present a method for computing free-energy differences using thermodynamic integration with a neural network potential that interpolates between two target Hamiltonians. The int…
Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models
Bálint Máté, François Fleuret, Tristan Bereau
Thermodynamic integration (TI) offers a rigorous method for estimating free-energy differences by integrating over a sequence of interpolating conformational ensembles. However, TI…
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…
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…
Learning Interpolations between Boltzmann Densities
Bálint Máté, François Fleuret
We introduce a training objective for continuous normalizing flows that can be used in the absence of samples but in the presence of an energy function. Our method relies on either…
Deformations of Boltzmann Distributions
Bálint Máté, François Fleuret
Consider a one-parameter family of Boltzmann distributions . This work studies the problem of sampling from by first sampling from $p_…
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…
SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics
Atul Kumar Sinha, Daniele Paliotta, Bálint Máté +4
Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors. Detailed simulation frameworks such as the gold standard Gea…