Stéphane d'Ascoli
Stéphane d'Ascoli
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Jamming transition as a paradigm to understand the loss landscape of deep neural networks
Mario Geiger
,
Stefano Spigler
,
Stéphane d'Ascoli
,
Levent Sagun
,
Marco Baity-Jesi
,
Giulio Biroli
,
Matthieu Wyart
January 2019
Cite
Arxiv
Phys. Rev. E
Type
Journal article
Publication
Physical Review E
Related
Scaling description of generalization with number of parameters in deep learning
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
Triple descent and the two kinds of overfitting: where & why do they appear?
Double Trouble in Double Descent: Bias and Variance (s) in the Lazy Regime
Conditioned Query Generation for Task-Oriented Dialogue Systems
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