| Entropy and mutual information in models of deep neural networks M Gabrié, A Manoel, C Luneau, J Barbier, N Macris, F Krzakala, ... Advances in Neural Information Processing Systems 2018 (31), 1821--183, 2019 | 211 | 2019 |
| Adaptive Monte Carlo augmented with normalizing flows M Gabrié, GM Rotskoff, E Vanden-Eijnden Proceedings of the National Academy of Sciences 119 (10), e2109420119, 2022 | 133 | 2022 |
| Modern applications of machine learning in quantum sciences A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ... arXiv preprint arXiv:2204.04198, 2022 | 71 | 2022 |
| Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy M Gabrié, EW Tramel, F Krzakala Advances in Neural Information Processing Systems, 640-648, 2015 | 64 | 2015 |
| Mean-field inference methods for neural networks M Gabrié Journal of Physics A: Mathematical and Theoretical 53 (22), 223002, 2020 | 50 | 2020 |
| Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines EW Tramel, M Gabrié, A Manoel, F Caltagirone, F Krzakala Physical Review X 8 (4), 041006, 2018 | 42 | 2018 |
| Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods M Gabrié, GM Rotskoff, E Vanden-Eijnden ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit …, 2021 | 26 | 2021 |
| Inferring sparsity: Compressed sensing using generalized restricted Boltzmann machines EW Tramel, A Manoel, F Caltagirone, M Gabrié, F Krzakala 2016 IEEE Information Theory Workshop (ITW), 265-269, 2016 | 26 | 2016 |
| Phase transitions in the q-coloring of random hypergraphs M Gabrié, V Dani, G Semerjian, L Zdeborová Journal of Physics A: Mathematical and Theoretical 50 (50), 505002, 2017 | 23 | 2017 |
| On the interplay between data structure and loss function in classification problems S d'Ascoli, M Gabrié, L Sagun, G Biroli Advances in Neural Information Processing Systems, 2021, 2021 | 22 | 2021 |
| Local-Global MCMC kernels: the best of both worlds S Samsonov, E Lagutin, M Gabrié, A Durmus, A Naumov, E Moulines Advances in Neural Information Processing Systems, 2021 | 16 | 2021 |
| Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging H Lawrence, DA Barmherzig, H Li, M Eickenberg, M Gabrié Proceedings of Machine Learning Research, MSML 107, 2020 | 14 | 2020 |
| On Sampling with Approximate Transport Maps L Grenioux, A Durmus, É Moulines, M Gabrié arXiv preprint arXiv:2302.04763, 2023 | 13 | 2023 |
| Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals J Brofos, M Gabrié, MA Brubaker, RR Lederman International Conference on Artificial Intelligence and Statistics, 5949-5986, 2022 | 9 | 2022 |
| Neural networks: From the perceptron to deep nets M Gabrié, S Ganguli, C Lucibello, R Zecchina Spin Glass Theory and Far Beyond: Replica Symmetry Breaking After 40 Years …, 2023 | 8 | 2023 |
| Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks C Domingo-Enrich, A Bietti, M Gabrié, J Bruna, E Vanden-Eijnden arXiv preprint arXiv:2107.05134, 2021 | 7 | 2021 |
| Towards an understanding of neural networks: mean-field incursions M Gabrié Université Paris sciences et lettres, 2019 | 7 | 2019 |
| Stochastic Localization via Iterative Posterior Sampling L Grenioux, M Noble, M Gabrié, AO Durmus arXiv preprint arXiv:2402.10758, 2024 | 4 | 2024 |
| Balanced Training of Energy-Based Models with Adaptive Flow Sampling L Grenioux, É Moulines, M Gabrié arXiv preprint arXiv:2306.00684, 2023 | 4 | 2023 |
| flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX M Gabrié, D Foreman-Mackey Journal of Open Source Software 8 (83), 5021, 2023 | 4 | 2023 |
Florent KrzakalaÉcole polytechnique fédérale de LausanneEmail verificata su epfl.ch