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2020 – today
- 2024
[c11]Lucas Stoffl, Andy Bonnetto, Stéphane d'Ascoli, Alexander Mathis
:
Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders. ECCV (64) 2024: 106-125- [c10]Stéphane d'Ascoli, Sören Becker, Philippe Schwaller, Alexander Mathis, Niki Kilbertus:
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. ICLR 2024 - 2023
- [i18]Samy Jelassi, Stéphane d'Ascoli, Carles Domingo-Enrich, Yuhuai Wu, Yuanzhi Li, François Charton:
Length Generalization in Arithmetic Transformers. CoRR abs/2306.15400 (2023) - [i17]Stéphane d'Ascoli, Samy Bengio, Josh M. Susskind, Emmanuel Abbe:
Boolformer: Symbolic Regression of Logic Functions with Transformers. CoRR abs/2309.12207 (2023) - [i16]Stéphane d'Ascoli, Sören Becker, Alexander Mathis, Philippe Schwaller, Niki Kilbertus:
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers. CoRR abs/2310.05573 (2023) - 2022
- [c9]Stéphane d'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, François Charton:
Deep symbolic regression for recurrence prediction. ICML 2022: 4520-4536 - [c8]Pierre-Alexandre Kamienny, Stéphane d'Ascoli, Guillaume Lample, François Charton:
End-to-end Symbolic Regression with Transformers. NeurIPS 2022 - [i15]Stéphane d'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, François Charton:
Deep Symbolic Regression for Recurrent Sequences. CoRR abs/2201.04600 (2022) - [i14]Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli:
Optimal learning rate schedules in high-dimensional non-convex optimization problems. CoRR abs/2202.04509 (2022) - [i13]Pierre-Alexandre Kamienny, Stéphane d'Ascoli, Guillaume Lample, François Charton:
End-to-end symbolic regression with transformers. CoRR abs/2204.10532 (2022) - 2021
- [c7]Stéphane d'Ascoli, Hugo Touvron, Matthew L. Leavitt, Ari S. Morcos, Giulio Biroli, Levent Sagun:
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases. ICML 2021: 2286-2296 - [c6]Maria Refinetti, Stéphane d'Ascoli, Ruben Ohana, Sebastian Goldt:
Align, then memorise: the dynamics of learning with feedback alignment. ICML 2021: 8925-8935 - [c5]Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli:
On the interplay between data structure and loss function in classification problems. NeurIPS 2021: 8506-8517 - [i12]Stéphane d'Ascoli, Marylou Gabrié, Levent Sagun, Giulio Biroli:
More data or more parameters? Investigating the effect of data structure on generalization. CoRR abs/2103.05524 (2021) - [i11]Stéphane d'Ascoli, Hugo Touvron, Matthew L. Leavitt, Ari S. Morcos, Giulio Biroli, Levent Sagun:
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases. CoRR abs/2103.10697 (2021) - [i10]Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Ari Morcos:
Transformed CNNs: recasting pre-trained convolutional layers with self-attention. CoRR abs/2106.05795 (2021) - 2020
- [c4]Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli, Florent Krzakala:
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime. ICML 2020: 2280-2290 - [c3]Stéphane d'Ascoli, Levent Sagun, Giulio Biroli:
Triple descent and the two kinds of overfitting: where & why do they appear? NeurIPS 2020 - [c2]Stéphane d'Ascoli, Alice Coucke, Francesco Caltagirone, Alexandre Caulier, Marc Lelarge:
Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems. SLSP 2020: 23-34 - [i9]Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli, Florent Krzakala:
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime. CoRR abs/2003.01054 (2020) - [i8]Stéphane d'Ascoli, Levent Sagun, Giulio Biroli:
Triple descent and the two kinds of overfitting: Where & why do they appear? CoRR abs/2006.03509 (2020) - [i7]Stéphane d'Ascoli, Alice Coucke, Francesco Caltagirone, Alexandre Caulier, Marc Lelarge:
Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems. CoRR abs/2011.02143 (2020) - [i6]Maria Refinetti, Stéphane d'Ascoli, Ruben Ohana, Sebastian Goldt:
The dynamics of learning with feedback alignment. CoRR abs/2011.12428 (2020)
2010 – 2019
- 2019
- [c1]Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Joan Bruna:
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias. NeurIPS 2019: 9330-9340 - [i5]Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, Matthieu Wyart:
Scaling description of generalization with number of parameters in deep learning. CoRR abs/1901.01608 (2019) - [i4]Stéphane d'Ascoli, Levent Sagun, Joan Bruna, Giulio Biroli:
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias. CoRR abs/1906.06766 (2019) - [i3]Stéphane d'Ascoli, Alice Coucke, Francesco Caltagirone, Alexandre Caulier, Marc Lelarge:
Conditioned Query Generation for Task-Oriented Dialogue Systems. CoRR abs/1911.03698 (2019) - 2018
- [i2]Mario Geiger, Stefano Spigler, Stéphane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart:
The jamming transition as a paradigm to understand the loss landscape of deep neural networks. CoRR abs/1809.09349 (2018) - [i1]Stefano Spigler, Mario Geiger, Stéphane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart:
A jamming transition from under- to over-parametrization affects loss landscape and generalization. CoRR abs/1810.09665 (2018)
Coauthor Index
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last updated on 2024-12-10 20:48 CET by the dblp team
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