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Scaling description of generalization with number of parameters in deep learning

Journal of statistical mechanics, 2020-02, Vol.2020 (2), p.23401

2020

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Electromagnetic Emission from Supermassive Binary Black Holes Approaching Merger

The Astrophysical journal, 2018-10, Vol.865 (2), p.140

2018

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

Physical review. E, 2019-07, Vol.100 (1-1), p.012115-012115, Article 012115

2019

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Align, then memorise: the dynamics of learning with feedback alignment

Journal of physics. A, Mathematical and theoretical, 2022-01, Vol.55 (4), p.44002

2022

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Align, then memorise: the dynamics of learning with feedback alignment

Journal of statistical mechanics, 2022-11, Vol.2022 (11), p.114002

2022

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

Journal of statistical mechanics, 2021-12, Vol.2021 (12), p.124002

2021

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A jamming transition from under- to over-parametrization affects generalization in deep learning

Journal of physics. A, Mathematical and theoretical, 2019-11, Vol.52 (47), p.474001

2019

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ConViT: improving vision transformers with soft convolutional inductive biases

Journal of statistical mechanics, 2022-11, Vol.2022 (11), p.114005

2022

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Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems

Statistical Language and Speech Processing, p.23-34

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Align, then memorise: the dynamics of learning with feedback alignment This is a lightly edited version of an article that appeared previously in the proceedings of the 38th International Conference on Machine Learning (ICML) in 2021 [1]

Journal of physics. A, Mathematical and theoretical, 2022-01, Vol.55 (4)

2022

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Triple descent and the two kinds of overfitting: where and why do they appear? This article is an updated version of: Ascoli S, Sagun L and Biroli G 2020 Triple descent and the two kinds of overfitting: where and why do they appear? Advances in Neural Information Processing Systems vol 33 ed H Larochelle, M Ranzato, R Hadsell, M F Balcan and H Lin (New York: Curran Associates)

Journal of statistical mechanics, 2021-12, Vol.2021 (12)

2021

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Align, then memorise: the dynamics of learning with feedback alignment This article is an updated version of: Refinetti M, D’Ascoli S, Ohana R and Goldt S 2021 Align, then memorise: the dynamics of learning with feedback alignment Proc. 38th Int. Conf. Machine Learning vol 139 ed M Meila and T Zhang pp 8925–35

Journal of statistical mechanics, 2022-11, Vol.2022 (11)

2022

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ConViT: improving vision transformers with soft convolutional inductive biases This article is an updated version of: D’Ascoli S, Touvron H, Leavitt M L, Morcos A S, Biroli G and Sagun L 2021 ConViT: improving vision transformers with soft convolutional inductive biases Proc. 38th Int. Conf. Machine Learning vol 139 ed M Meila and T Zhang pp 2286–96

Journal of statistical mechanics, 2022-11, Vol.2022 (11)

2022

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Revue française de pédagogie, 2011-04 (175), p.5-14

2011

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Scaling description of generalization with number of parameters in deep learning

Electromagnetic Emission from Supermassive Binary Black Holes Approaching Merger

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

Align, then memorise: the dynamics of learning with feedback alignment

Align, then memorise: the dynamics of learning with feedback alignment

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

A jamming transition from under- to over-parametrization affects generalization in deep learning

ConViT: improving vision transformers with soft convolutional inductive biases

Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems

Align, then memorise: the dynamics of learning with feedback alignment This is a lightly edited version of an article that appeared previously in the proceedings of the 38th International Conference on Machine Learning (ICML) in 2021 [1]

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