| Framing RNN as a kernel method: A neural ODE approach A Fermanian, P Marion, JP Vert, G Biau Advances in Neural Information Processing Systems 34, 3121-3134, 2021 | 29 | 2021 |
| Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs P Marion, PK Nowak, F Piccinno EMNLP 2021, 8813–8829, 2021 | 24 | 2021 |
| A tool for custom construction of QMC and RQMC point sets P L’Ecuyer, P Marion, M Godin, F Puchhammer International Conference on Monte Carlo and Quasi-Monte Carlo Methods in …, 2020 | 22 | 2020 |
| Generalization bounds for neural ordinary differential equations and deep residual networks P Marion Advances in Neural Information Processing Systems 36, 2023 | 12 | 2023 |
| Scaling resnets in the large-depth regime P Marion, A Fermanian, G Biau, JP Vert arXiv preprint arXiv:2206.06929, 2022 | 12 | 2022 |
| An algorithm to compute the t-value of a digital net and of its projections P Marion, M Godin, P L’Ecuyer Journal of Computational and Applied Mathematics 371, 112669, 2020 | 10 | 2020 |
| Implicit regularization of deep residual networks towards neural ODEs P Marion, YH Wu, ME Sander, G Biau The Twelfth International Conference on Learning Representations, 2023 | 8 | 2023 |
| Leveraging the two-timescale regime to demonstrate convergence of neural networks P Marion, R Berthier Advances in Neural Information Processing Systems 36, 2023 | 6 | 2023 |
| Implicit Diffusion: Efficient Optimization through Stochastic Sampling P Marion, A Korba, P Bartlett, M Blondel, V De Bortoli, A Doucet, ... arXiv preprint arXiv:2402.05468, 2024 | 1 | 2024 |
| Deep linear networks for regression are implicitly regularized towards flat minima P Marion, L Chizat arXiv preprint arXiv:2405.13456, 2024 | | 2024 |
| Mathematics of deep learning: generalization, optimization, continuous-time models P Marion Sorbonne Université, 2023 | | 2023 |
Gérard BiauProfessor, Sorbonne University, ParisVerified email at sorbonne-universite.fr
