Priming PCA with EigenGame. - Bálint Máté; François Fleuret (CoRR, 2021)

Flowification: Everything is a normalizing flow. - Bálint Máté; Samuel Klein; Tobias Golling; François Fleuret (NeurIPS, 2022)

SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics. - Atul Kumar Sinha; Daniele Paliotta; Bálint Máté; Sebastian Pina-Otey; John A. Raine; Tobias Golling; François Fleuret (CoRR, 2022)

Beyond Ansätze: Learning Quantum Circuits as Unitary Operators. - Bálint Máté; Bertrand Le Saux; Maxwell Henderson (CoRR, 2022)

Deformation Theory of Boltzmann Distributions. - Bálint Máté; François Fleuret (CoRR, 2022)

SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics. - Atul Kumar Sinha; Daniele Paliotta; Bálint Máté; John A. Raine; Tobias Golling; François Fleuret (NeurIPS, 2023)

Learning Deformation Trajectories of Boltzmann Densities. - Bálint Máté; François Fleuret (CoRR, 2023)

Graph Neural Networks Go Forward-Forward. - Daniele Paliotta; Mathieu Alain; Bálint Máté; François Fleuret (CoRR, 2023)

Learning Interpolations between Boltzmann Densities. - Bálint Máté; François Fleuret (Trans. Mach. Learn. Res., 2023)

Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows. - Bálint Máté; François Fleuret (CoRR, 2024)

Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models. - Bálint Máté; François Fleuret; Tristan Bereau (CoRR, 2024)

Solvation Free Energies from Neural Thermodynamic Integration. - Bálint Máté; François Fleuret; Tristan Bereau (CoRR, 2024)

Accurate and scalable exchange-correlation with deep learning. - Giulia Luise; Chin-Wei Huang; Thijs Vogels; Derk P. Kooi; Sebastian Ehlert; Stephanie Lanius; Klaas J. H. Giesbertz; Amir Karton; Deniz Gunceler; Megan Stanley; Wessel P. Bruinsma; Lin Huang; et al. (CoRR, 2025)