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Sebastian Goldt
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Cited by
Year
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
154*2019
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
1022019
Stochastic thermodynamics of resetting
J Fuchs*, S Goldt*, U Seifert
EPL (Europhysics Letters) 113 (6), 60009, 2016
1002016
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
71*2021
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
66*2022
Stochastic thermodynamics of learning
S Goldt, U Seifert
Physical review letters 118 (1), 010601, 2017
532017
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
422021
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
29*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
272021
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
182013
Thermodynamic efficiency of learning a rule in neural networks
S Goldt, U Seifert
New Journal of Physics 19 (11), 113001, 2017
162017
Data-driven emergence of convolutional structure in neural networks
A Ingrosso, S Goldt
Proceedings of the National Academy of Sciences 119 (40), e2201854119, 2022
102022
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
102019
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
52014
Neural networks trained with SGD learn distributions of increasing complexity
M Refinetti, A Ingrosso, S Goldt
arXiv preprint arXiv:2211.11567, 2022
22022
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
22022
The dynamics of representation learning in shallow, non-linear autoencoders
M Refinetti, S Goldt
International Conference on Machine Learning 18499-18519, 2022
22022
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
12023
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
1*2022
Attacks on Online Learners: a Teacher-Student Analysis
RG Margiotta, S Goldt, G Sanguinetti
arXiv preprint arXiv:2305.11132, 2023
2023
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