| De novo design of protein structure and function with RFdiffusion JL Watson, D Juergens, NR Bennett, BL Trippe, J Yim, HE Eisenach, ... Nature 620 (7976), 1089-1100, 2023 | 919 | 2023 |
| Diffusion schrödinger bridge with applications to score-based generative modeling V De Bortoli, J Thornton, J Heng, A Doucet Advances in Neural Information Processing Systems 34, 17695-17709, 2021 | 429 | 2021 |
| Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models JL Watson, D Juergens, NR Bennett, BL Trippe, J Yim, HE Eisenach, ... BioRxiv, 2022.12. 09.519842, 2022 | 212 | 2022 |
| SE (3) diffusion model with application to protein backbone generation J Yim, BL Trippe, V De Bortoli, E Mathieu, A Doucet, R Barzilay, ... arXiv preprint arXiv:2302.02277, 2023 | 188 | 2023 |
| Riemannian score-based generative modelling V De Bortoli, E Mathieu, M Hutchinson, J Thornton, YW Teh, A Doucet Advances in Neural Information Processing Systems 35, 2406-2422, 2022 | 183 | 2022 |
| Convergence of denoising diffusion models under the manifold hypothesis V De Bortoli arXiv preprint arXiv:2208.05314, 2022 | 163 | 2022 |
| A continuous time framework for discrete denoising models A Campbell, J Benton, V De Bortoli, T Rainforth, G Deligiannidis, ... Advances in Neural Information Processing Systems 35, 28266-28279, 2022 | 132 | 2022 |
| Bayesian imaging using plug & play priors: when langevin meets tweedie R Laumont, VD Bortoli, A Almansa, J Delon, A Durmus, M Pereyra SIAM Journal on Imaging Sciences 15 (2), 701-737, 2022 | 128 | 2022 |
| Diffusion Schrödinger bridge matching Y Shi, V De Bortoli, A Campbell, A Doucet Advances in Neural Information Processing Systems 36, 2024 | 112 | 2024 |
| Linear convergence bounds for diffusion models via stochastic localization J Benton, V De Bortoli, A Doucet, G Deligiannidis arXiv preprint arXiv:2308.03686, 2023 | 92 | 2023 |
| Wavelet score-based generative modeling F Guth, S Coste, V De Bortoli, S Mallat Advances in neural information processing systems 35, 478-491, 2022 | 63 | 2022 |
| Maximum likelihood estimation of regularization parameters in high-dimensional inverse problems: An empirical bayesian approach part i: Methodology and experiments AF Vidal, V De Bortoli, M Pereyra, A Durmus SIAM Journal on Imaging Sciences 13 (4), 1945-1989, 2020 | 60 | 2020 |
| Conditional simulation using diffusion Schrödinger bridges Y Shi, V De Bortoli, G Deligiannidis, A Doucet Uncertainty in Artificial Intelligence, 1792-1802, 2022 | 55 | 2022 |
| Can push-forward generative models fit multimodal distributions? A Salmona, V De Bortoli, J Delon, A Desolneux Advances in Neural Information Processing Systems 35, 10766-10779, 2022 | 45 | 2022 |
| Efficient stochastic optimisation by unadjusted Langevin Monte Carlo: Application to maximum marginal likelihood and empirical Bayesian estimation V De Bortoli, A Durmus, M Pereyra, AF Vidal Statistics and Computing 31, 1-18, 2021 | 45 | 2021 |
| Nearly d-linear convergence bounds for diffusion models via stochastic localization J Benton, VD Bortoli, A Doucet, G Deligiannidis OpenReview, 2024 | 44 | 2024 |
| Convergence rates and approximation results for SGD and its continuous-time counterpart X Fontaine, V De Bortoli, A Durmus Conference on Learning Theory, 1965-2058, 2021 | 40* | 2021 |
| Approximate Bayesian computation with the sliced-Wasserstein distance K Nadjahi, V De Bortoli, A Durmus, R Badeau, U Şimşekli ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 39 | 2020 |
| Simulating diffusion bridges with score matching J Heng, V De Bortoli, A Doucet, J Thornton arXiv preprint arXiv:2111.07243, 2021 | 38 | 2021 |
| Quantitative propagation of chaos for SGD in wide neural networks V De Bortoli, A Durmus, X Fontaine, U Simsekli Advances in Neural Information Processing Systems 33, 278-288, 2020 | 36 | 2020 |
Arnaud DoucetGoogle DeepMind確認したメール アドレス: google.com