| Convit: Improving vision transformers with soft convolutional inductive biases S d’Ascoli, H Touvron, ML Leavitt, AS Morcos, G Biroli, L Sagun International Conference on Machine Learning, 2286-2296, 2021 | 955 | 2021 |
| Scaling description of generalization with number of parameters in deep learning M Geiger, A Jacot, S Spigler, F Gabriel, L Sagun, S d’Ascoli, G Biroli, ... Journal of Statistical Mechanics: Theory and Experiment 2020 (2), 023401, 2020 | 236 | 2020 |
| End-to-end symbolic regression with transformers PA Kamienny, S d'Ascoli, G Lample, F Charton Advances in Neural Information Processing Systems, 8754-8765, 2022 | 186 | 2022 |
| Double trouble in double descent: Bias and variance (s) in the lazy regime S d’Ascoli, M Refinetti, G Biroli, F Krzakala International Conference on Machine Learning, 2280-2290, 2020 | 185 | 2020 |
| Jamming transition as a paradigm to understand the loss landscape of deep neural networks M Geiger, S Spigler, S d'Ascoli, L Sagun, M Baity-Jesi, G Biroli, M Wyart Physical Review E 100 (1), 012115, 2019 | 180 | 2019 |
| A jamming transition from under-to over-parametrization affects generalization in deep learning S Spigler, M Geiger, S d’Ascoli, L Sagun, G Biroli, M Wyart Journal of Physics A: Mathematical and Theoretical 52 (47), 474001, 2019 | 160 | 2019 |
| Electromagnetic Emission from Supermassive Binary Black Holes Approaching Merger S d'Ascoli, SC Noble, DB Bowen, M Campanelli, JH Krolik, V Mewes The Astrophysical Journal 865 (2), 2018 | 123 | 2018 |
| Triple descent and the two kinds of overfitting: Where & why do they appear? S d'Ascoli, L Sagun, G Biroli Advances in Neural Information Processing Systems 33, 3058-3069, 2020 | 100 | 2020 |
| Deep symbolic regression for recurrence prediction S d’Ascoli, PA Kamienny, G Lample, F Charton International Conference on Machine Learning, 4520-4536, 2022 | 80* | 2022 |
| A jamming transition from under-to over-parametrization affects loss landscape and generalization S Spigler, M Geiger, S d'Ascoli, L Sagun, G Biroli, M Wyart NeurIPS 2018 Workshop « Science of Deep Leaning Meets Engineering », 2019 | 76 | 2019 |
| Align, then memorise: the dynamics of learning with feedback alignment M Refinetti, S d’Ascoli, R Ohana, S Goldt International Conference on Machine Learning, 8925-8935, 2021 | 56 | 2021 |
| Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias S d'Ascoli, L Sagun, G Biroli, J Bruna Advances in Neural Information Processing Systems, 9334-9345, 2019 | 45 | 2019 |
| Length generalization in arithmetic transformers S Jelassi, S d'Ascoli, C Domingo-Enrich, Y Wu, Y Li, F Charton arXiv preprint arXiv:2306.15400, 2023 | 35 | 2023 |
| On the interplay between data structure and loss function in classification problems S d'Ascoli, M Gabrié, L Sagun, G Biroli Advances in Neural Information Processing Systems 34, 2021 | 25 | 2021 |
| Odeformer: Symbolic regression of dynamical systems with transformers S d'Ascoli, S Becker, A Mathis, P Schwaller, N Kilbertus arXiv preprint arXiv:2310.05573, 2023 | 19 | 2023 |
| Optimal learning rate schedules in high-dimensional non-convex optimization problems S d'Ascoli, M Refinetti, G Biroli arXiv preprint arXiv:2202.04509, 2022 | 8 | 2022 |
| Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems S d’Ascoli, A Coucke, F Caltagirone, A Caulier, M Lelarge International Conference on Statistical Language and Speech Processing, 23-34, 2020 | 8* | 2020 |
| Transformed CNNs: recasting pre-trained convolutional layers with self-attention S d'Ascoli, L Sagun, G Biroli, A Morcos arXiv preprint arXiv:2106.05795, 2021 | 7 | 2021 |
| Boolformer: Symbolic regression of logic functions with transformers S d'Ascoli, S Bengio, J Susskind, E Abbé arXiv preprint arXiv:2309.12207, 2023 | 5 | 2023 |
| Comprendre la révolution de l'intelligence artificielle S d'Ascoli First, 2020 | 4 | 2020 |
Giulio BiroliProfessor of Theoretical Physics, ENS ParisOverená e-mailová adresa na: cea.fr
