| Publications Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals. [pdf] Brofos J. A., Gabrié M., Brubaker M. A., & Lederman R. R. arXiv:2110.13216 (2021) Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods. [pdf] Gabrié M., Rotskoff G. M., & Vanden-Eijnden E. Invertible Neural Networks, NormalizingFlows, and Explicit Likelihood Models (ICML Workshop) (2021) - Accepted for contributed talk. Adaptive Monte Carlo augmented with normalizing flows [pdf] Gabrié M., Rotskoff G. M., Vanden-Eijnden E. arXiv:2105.12603 (2021) On the interplay between data structure and loss function in classification problems [pdf] d'Ascoli S., Gabrié M., Sagun L., Biroli G. arXiv:2103.05524 (2021) - Accepted at NeurIPS 2021 Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging [pdf] Lawrence H., Barmherzig D. A., Li H., Eickenberg M., Gabrié M. Proceedings of Machine Learning Research, 107, 1-31, MSML (2021) Mean-field inference methods for neural networks, [pdf] Gabrié, M. Journal of Physics A: Mathematical and Theoretical, 53(22), 1–58. (2020) Blind calibration for compressed sensing: State evolution and an online algorithm, [pdf] Gabrié, M., Barbier, J., Krzakala, F., & Zdeborová, L. Journal of Physics A: Mathematical and Theoretical, 53(33), 334004. (2020) Entropy and mutual information in models of deep neural networks, [pdf] [spotlight short video] Gabrié, M., Manoel, A., Luneau, C., Barbier, J., Macris, N., Krzakala, F., & Zdeborová, L. Advances in Neural Information Processing Systems 31, 1826--1836 (2018) Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines, [pdf] Tramel, E. W., Gabrié, M., Manoel, A., Caltagirone, F., & Krzakala, F. Advances in Neural Physical Review X, 8(4), 041006. (2018) Phase transitions in the q-coloring of random hypergraphs, [pdf] Gabrié, M., Dani, V., Semerjian, G., & Zdeborová, L. Journal of Physics A: Mathematical and Theoretical, 50(50). (2017) Inferring sparsity: Compressed sensing using generalized restricted Boltzmann machines, [pdf] Tramel, E. W., Manoel, A., Caltagirone, F., Gabrié, M., & Krzakala, F. IEEE Information Theory Workshop (ITW), 265–269. (2016) Training Restricted Boltzmann Machines via the Thouless-Anderson-Palmer Free Energy, [pdf] Gabrié, M., Tramel, E. W., & Krzakala, F. Advances in Neural Information Processing Systems 28, 640--648. (2015) |