| A simple linear algebra identity to optimize large-scale neural network quantum states R Rende, LL Viteritti, L Bardone, F Becca, S Goldt Communications Physics 7 (1), 260, 2024 | 23* | 2024 |
| Align, then memorise: the dynamics of learning with feedback alignment M Refinetti, S d’Ascoli, R Ohana, S Goldt International Conference on Machine Learning, 8925-8935, 2021 | 46 | 2021 |
| Attacks on Online Learners: a Teacher-Student Analysis RG Margiotta, S Goldt, G Sanguinetti Advances in Neural Information Processing Systems 36, 2023 | 3 | 2023 |
| Bayesian reconstruction of memories stored in neural networks from their connectivity S Goldt, F Krzakala, L Zdeborová, N Brunel PLOS Computational Biology 19 (1), e1010813, 2023 | 2 | 2023 |
| Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed M Refinetti, S Goldt, F Krzakala, L Zdeborová International Conference on Machine Learning, 8936-8947, 2021 | 79 | 2021 |
| Continual learning in the teacher-student setup: Impact of task similarity S Lee, S Goldt, A Saxe International Conference on Machine Learning, 6109-6119, 2021 | 62 | 2021 |
| Data-driven emergence of convolutional structure in neural networks A Ingrosso, S Goldt Proceedings of the National Academy of Sciences 119 (40), e2201854119, 2022 | 35 | 2022 |
| Diverse perceptual biases emerge from Hebbian plasticity in a recurrent neural network model F Schönsberg, D Giana, Y Chopra, ME Diamond, S Goldt bioRxiv, 2024.05. 30.596641, 2024 | | 2024 |
| Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup S Goldt, MS Advani, AM Saxe, F Krzakala, L Zdeborová Advances in Neural Information Processing Systems 32, 6979--6989, 2019 | 153 | 2019 |
| Fine-tuning Neural Network Quantum States R Rende, S Goldt, F Becca, LL Viteritti arXiv preprint arXiv:2403.07795, 2024 | 2 | 2024 |
| Generalisation dynamics of online learning in over-parameterised neural networks S Goldt, MS Advani, AM Saxe, F Krzakala, L Zdeborová ICML 2019 Workshop on Theoretical Physics for Deep Learning, 2019 | 13 | 2019 |
| 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, ... Advances in Neural Information Processing Systems 34, 18137-18151, 2021 | 163* | 2021 |
| Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networks E Székely, L Bardone, F Gerace, S Goldt arXiv preprint arXiv:2312.14922, 2023 | 1 | 2023 |
| Mapping of attention mechanisms to a generalized Potts model R Rende, F Gerace, A Laio, S Goldt Physical Review Research 6 (2), 023057, 2024 | 11* | 2024 |
| Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation S Lee, SS Mannelli, C Clopath, S Goldt, A Saxe International Conference on Machine Learning, PMLR 162:12455-12477, 2022 | 12 | 2022 |
| Modelling 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, 2019 | 211* | 2019 |
| Neural networks trained with SGD learn distributions of increasing complexity M Refinetti, A Ingrosso, S Goldt International Conference on Machine Learning, 28843-28863, 2023 | 24 | 2023 |
| Perspectives on adaptive dynamical systems J Sawicki, R Berner, SAM Loos, M Anvari, R Bader, W Barfuss, N Botta, ... Chaos 33, 071501, 2023 | 22 | 2023 |
| Quantifying lottery tickets under label noise: accuracy, calibration, and complexity V Arora, D Irto, S Goldt, G Sanguinetti Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 216:88-98, 2023 | 1 | 2023 |
| Redundant representations help generalization in wide neural networks D Doimo, A Glielmo, S Goldt, A Laio Advances in Neural Information Processing Systems 35, in press, 2022 | 8* | 2022 |
Florent KrzakalaÉcole polytechnique fédérale de LausanneVerified email at epfl.ch
