| 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 | 105* | 2019 |
| Stochastic thermodynamics of resetting J Fuchs*, S Goldt*, U Seifert EPL (Europhysics Letters) 113 (6), 60009, 2016 | 84 | 2016 |
| 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 | 69 | 2019 |
| Stochastic thermodynamics of learning S Goldt, U Seifert Physical review letters 118 (1), 010601, 2017 | 48 | 2017 |
| 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 | 41* | 2022 |
| 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 | 28* | 2021 |
| 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 | 18 | 2013 |
| 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 | 13 | 2021 |
| Thermodynamic efficiency of learning a rule in neural networks S Goldt, U Seifert New Journal of Physics 19 (11), 113001, 2017 | 12 | 2017 |
| 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 | 11* | 2021 |
| 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 | 8 | 2019 |
| 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 | 7 | 2021 |
| Role of the potential landscape on the single-file diffusion through channels SD Goldt, EM Terentjev The Journal of Chemical Physics 141 (22), 224901, 2014 | 5 | 2014 |
| Data-driven emergence of convolutional structure in neural networks A Ingrosso, S Goldt arXiv preprint arXiv:2202.00565, 2022 | 1 | 2022 |
| The dynamics of representation learning in shallow, non-linear autoencoders M Refinetti, S Goldt arXiv preprint arXiv:2201.02115, 2022 | 1 | 2022 |
| Bayesian reconstruction of memories stored in neural networks from their connectivity S Goldt, F Krzakala, L Zdeborová, N Brunel arXiv preprint arXiv:2105.07416, 2021 | 1 | 2021 |
| The impact of memory on learning sequence-to-sequence tasks A Seif, SAM Loos, G Tucci, É Roldán, S Goldt arXiv preprint arXiv:2205.14683, 2022 | | 2022 |
| Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation S Lee, SS Mannelli, C Clopath, S Goldt, A Saxe arXiv preprint arXiv:2205.09029, 2022 | | 2022 |
| Representation mitosis in wide neural networks D Doimo, A Glielmo, S Goldt, A Laio arXiv preprint arXiv:2106.03485, 2021 | | 2021 |
Florent KrzakalaÉcole polytechnique fédérale de Lausanne確認したメール アドレス: epfl.ch
