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How isotropic kernels learn simple invariants

Jun 29, 2020
Jonas Paccolat, Stefano Spigler, Matthieu Wyart

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Disentangling feature and lazy learning in deep neural networks: an empirical study

Jun 19, 2019
Mario Geiger, Stefano Spigler, Arthur Jacot, Matthieu Wyart

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Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm

Jun 06, 2019
Stefano Spigler, Mario Geiger, Matthieu Wyart

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Scaling description of generalization with number of parameters in deep learning

Jan 18, 2019
Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, Matthieu Wyart

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A jamming transition from under- to over-parametrization affects loss landscape and generalization

Oct 22, 2018
Stefano Spigler, Mario Geiger, Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart

* 11 pages, 6 figures, submitted to NIPS workshop "Integration of Deep Learning Theories". arXiv admin note: substantial text overlap with arXiv:1809.09349 

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The jamming transition as a paradigm to understand the loss landscape of deep neural networks

Oct 03, 2018
Mario Geiger, Stefano Spigler, Stéphane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart

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