| Machine learning and the physical sciences G Carleo, I Cirac, K Cranmer, L Daudet, M Schuld, N Tishby, ... Reviews of Modern Physics 91 (4), 045002, 2019 | 2327 | 2019 |
| Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications A Decelle, F Krzakala, C Moore, L Zdeborová Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 84 (6 …, 2011 | 939 | 2011 |
| Spectral redemption in clustering sparse networks F Krzakala, C Moore, E Mossel, J Neeman, A Sly, L Zdeborová, P Zhang Proceedings of the National Academy of Sciences 110 (52), 20935-20940, 2013 | 740 | 2013 |
| Gibbs states and the set of solutions of random constraint satisfaction problems F Krzakała, A Montanari, F Ricci-Tersenghi, G Semerjian, L Zdeborová Proceedings of the National Academy of Sciences 104 (25), 10318-10323, 2007 | 540 | 2007 |
| Statistical physics of inference: Thresholds and algorithms L Zdeborová, F Krzakala Advances in Physics 65 (5), 453-552, 2016 | 493 | 2016 |
| Inference and phase transitions in the detection of modules in sparse networks A Decelle, F Krzakala, C Moore, L Zdeborová Physical Review Letters 107 (6), 065701, 2011 | 424 | 2011 |
| Inferring the origin of an epidemic with a dynamic message-passing algorithm AY Lokhov, M Mézard, H Ohta, L Zdeborová Physical Review E 90 (1), 012801, 2014 | 360 | 2014 |
| Statistical-physics-based reconstruction in compressed sensing F Krzakala, M Mézard, F Sausset, YF Sun, L Zdeborová Physical Review X 2 (2), 021005, 2012 | 345 | 2012 |
| Probabilistic reconstruction in compressed sensing: algorithms, phase diagrams, and threshold achieving matrices F Krzakala, M Mézard, F Sausset, Y Sun, L Zdeborová Journal of Statistical Mechanics: Theory and Experiment 2012 (08), P08009, 2012 | 318 | 2012 |
| Optimal errors and phase transitions in high-dimensional generalized linear models J Barbier, F Krzakala, N Macris, L Miolane, L Zdeborová Proceedings of the National Academy of Sciences 116 (12), 5451-5460, 2019 | 310* | 2019 |
| Phase transitions in the coloring of random graphs L Zdeborová, F Krząkała Physical Review E 76 (3), 031131, 2007 | 300 | 2007 |
| Percolation on sparse networks B Karrer, MEJ Newman, L Zdeborová Physical review letters 113 (20), 208702, 2014 | 290 | 2014 |
| Network dismantling A Braunstein, L Dall’Asta, G Semerjian, L Zdeborová Proceedings of the National Academy of Sciences 113 (44), 12368-12373, 2016 | 257 | 2016 |
| Modeling the Influence of Data Structure on Learning in Neural Networks: The Hidden Manifold Model S Goldt, M Mézard, F Krzakala, L Zdeborová Physical Review X 10 (4), 041044, 2020 | 219* | 2020 |
| Entropy and mutual information in models of deep neural networks M Gabrié, A Manoel, C Luneau, J Barbier, N Macris, F Krzakala, ... Journal of Statistical Mechanics: Theory and Experiment 2019 (12), 124014, 2019 | 212 | 2019 |
| Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula J Barbier, M Dia, N Macris, F Krzakala, T Lesieur, L Zdeborová Advances in Neural Information Processing Systems, 424-432, 2016 | 199* | 2016 |
| Adaptive damping and mean removal for the generalized approximate message passing algorithm J Vila, P Schniter, S Rangan, F Krzakala, L Zdeborová 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 180 | 2015 |
| Spectral Clustering of graphs with the Bethe Hessian A Saade, F Krzakala, L Zdeborová Advances in Neural Information Processing Systems, 406-414, 2014 | 178 | 2014 |
| Generalisation error in learning with random features and the hidden manifold model F Gerace, B Loureiro, F Krzakala, M Mézard, L Zdeborová International Conference on Machine Learning, 3452-3462, 2020 | 176 | 2020 |
| Learning curves of generic features maps for realistic datasets with a teacher-student model B Loureiro, C Gerbelot, H Cui, S Goldt, F Krzakala, M Mezard, ... Thirty-Fifth Conference on Neural Information Processing Systems, 2021 | 170* | 2021 |
Florent KrzakalaÉcole polytechnique fédérale de LausanneVerified email at epfl.ch
