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ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases


Mar 19, 2021
Stéphane d'Ascoli, Hugo Touvron, Matthew Leavitt, Ari Morcos, Giulio Biroli, Levent Sagun


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More data or more parameters? Investigating the effect of data structure on generalization


Mar 09, 2021
Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli


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The dynamics of learning with feedback alignment


Nov 24, 2020
Maria Refinetti, Stéphane d'Ascoli, Ruben Ohana, Sebastian Goldt

* The accompanying code for this paper is available at https://github.com/sdascoli/dfa-dynamics 

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Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems


Nov 03, 2020
Stéphane d'Ascoli, Alice Coucke, Francesco Caltagirone, Alexandre Caulier, Marc Lelarge

* arXiv admin note: substantial text overlap with arXiv:1911.03698 

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Triple descent and the two kinds of overfitting: Where & why do they appear?


Jun 05, 2020
Stéphane d'Ascoli, Levent Sagun, Giulio Biroli


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Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime


Apr 03, 2020
Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli, Florent Krzakala

* 29 pages, 12 figures 

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Conditioned Query Generation for Task-Oriented Dialogue Systems


Nov 09, 2019
Stéphane d'Ascoli, Alice Coucke, Francesco Caltagirone, Alexandre Caulier, Marc Lelarge


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Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias


Jun 16, 2019
Stéphane d'Ascoli, Levent Sagun, Joan Bruna, Giulio Biroli


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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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