Seuraa
Marylou Gabrié
Marylou Gabrié
École Polytechnique, CMAP
Vahvistettu sähköpostiosoite verkkotunnuksessa polytechnique.edu - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
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
1842019
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
642022
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
592015
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
392018
Mean-field inference methods for neural networks
M Gabrié
Journal of Physics A: Mathematical and Theoretical 53 (22), 223002, 2020
342020
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
292022
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
272016
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
212017
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
16*2021
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
152021
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
142020
Towards an understanding of neural networks: mean-field incursions
M Gabrié
Paris Sciences et Lettres (ComUE), 2019
72019
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
62021
Neural networks: from the perceptron to deep nets
M Gabrié, S Ganguli, C Lucibello, R Zecchina
arXiv preprint arXiv:2304.06636, 2023
52023
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
32022
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
32021
On Sampling with Approximate Transport Maps
L Grenioux, A Durmus, É Moulines, M Gabrié
arXiv preprint arXiv:2302.04763, 2023
22023
flowMC: Normalizing-flow enhanced sampling package for probabilistic inference in Jax
KWK Wong, M Gabrié, D Foreman-Mackey
arXiv preprint arXiv:2211.06397, 2022
22022
Blind calibration for compressed sensing: State evolution and an online algorithm
M Gabrié, J Barbier, F Krzakala, L Zdeborová
Journal of Physics A: Mathematical and Theoretical 53 (33), 334004, 2020
12020
Optimizing Markov Chain Monte Carlo Convergence with Normalizing Flows and Gibbs Sampling
C Schönle, M Gabrié
NeurIPS 2023 AI for Science Workshop, 2023
2023
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Artikkelit 1–20