| 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 | 210* | 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 | 162* | 2021 |
| 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 |
| The gaussian equivalence of generative models for learning with shallow neural networks S Goldt, B Loureiro, G Reeves, F Krzakala, M Mézard, L Zdeborová Mathematical and Scientific Machine Learning, 426-471, 2022 | 130* | 2022 |
| Stochastic thermodynamics of resetting J Fuchs*, S Goldt*, U Seifert EPL (Europhysics Letters) 113 (6), 60009, 2016 | 127 | 2016 |
| 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 | 78 | 2021 |
| Stochastic thermodynamics of learning S Goldt, U Seifert Physical review letters 118 (1), 010601, 2017 | 66 | 2017 |
| 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 | 61 | 2021 |
| 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 | 45 | 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 | 33 | 2022 |
| A simple linear algebra identity to optimize large-scale neural network quantum states R Rende, LL Viteritti, L Bardone, F Becca, S Goldt arXiv preprint arXiv:2310.05715, 2023 | 23 | 2023 |
| Neural networks trained with SGD learn distributions of increasing complexity M Refinetti, A Ingrosso, S Goldt International Conference on Machine Learning, 28843-28863, 2023 | 22 | 2023 |
| Perspectives on adaptive dynamical systems J Sawicki, R Berner, SAM Loos, M Anvari, R Bader, W Barfuss, N Botta, ... Chaos 33, 071501, 2023 | 20 | 2023 |
| Zinc finger proteins and the 3D organization of chromosomes CJ Feinauer, A Hofmann, S Goldt, L Liu, G Mate, DW Heermann Advances in protein chemistry and structural biology 90, 67-117, 2013 | 19 | 2013 |
| Thermodynamic efficiency of learning a rule in neural networks S Goldt, U Seifert New Journal of Physics 19 (11), 113001, 2017 | 16 | 2017 |
| 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 |
| 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 |
| The dynamics of representation learning in shallow, non-linear autoencoders M Refinetti, S Goldt International Conference on Machine Learning 18499-18519, 2022 | 12 | 2022 |
| Transformer wave function for the shastry-sutherland model: emergence of a spin-liquid phase LL Viteritti, R Rende, A Parola, S Goldt, F Becca arXiv preprint arXiv:2311.16889, 2023 | 11 | 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 | 10* | 2024 |
Florent KrzakalaÉcole polytechnique fédérale de Lausanne在 epfl.ch 的电子邮件经过验证
