| Neural ordinary differential equations RTQ Chen, Y Rubanova, J Bettencourt, DK Duvenaud Advances in neural information processing systems, 6571-6583, 2018 | 6341 | 2018 |
| Isolating Sources of Disentanglement in Variational Autoencoders RTQ Chen, X Li, R Grosse, D Duvenaud Advances in Neural Information Processing Systems, NIPS 2018, 2018 | 1559 | 2018 |
| Latent odes for irregularly-sampled time series Y Rubanova, RTQ Chen, D Duvenaud Advances in Neural Information Processing Systems, NeurIPS 2019, 2019 | 1035* | 2019 |
| Flow Matching for Generative Modeling Y Lipman, RTQ Chen, H Ben-Hamu, M Nickel, M Le International Conference on Learning Representations, ICLR 2023, 2022 | 992 | 2022 |
| FFJORD: Free-form continuous dynamics for scalable reversible generative models W Grathwohl, RTQ Chen, J Betterncourt, I Sutskever, D Duvenaud International Conference on Learning Representations, ICLR 2019, 2019 | 967 | 2019 |
| Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International Conference on Machine Learning, ICML 2019, 2019 | 732 | 2019 |
| Fast patch-based style transfer of arbitrary style RTQ Chen, M Schmidt Constructive Machine Learning Workshop, NIPS 2016, 2016 | 463 | 2016 |
| Residual flows for invertible generative modeling RTQ Chen, J Behrmann, DK Duvenaud, JH Jacobsen Advances in Neural Information Processing Systems, 9913-9923, 2019 | 429 | 2019 |
| Scalable gradients for stochastic differential equations X Li, TKL Wong, RTQ Chen, D Duvenaud International Conference on Artificial Intelligence and Statistics, 3870-3882, 2020 | 368 | 2020 |
| Scalable reversible generative models with free-form continuous dynamics W Grathwohl, RTQ Chen, J Bettencourt, D Duvenaud International Conference on Learning Representations, 2019 | 159 | 2019 |
| Learning Neural Event Functions for Ordinary Differential Equations RTQ Chen, B Amos, M Nickel International Conference on Learning Representations, ICLR 2021, 2021 | 141 | 2021 |
| Neural Spatio-Temporal Point Processes RTQ Chen, B Amos, M Nickel International Conference on Learning Representations, ICLR 2021, 2021 | 125 | 2021 |
| Riemannian Flow Matching on General Geometries RTQ Chen, Y Lipman ICLR 2024, 2023 | 123* | 2023 |
| Multisample Flow Matching: Straightening Flows with Minibatch Couplings AA Pooladian, H Ben-Hamu, C Domingo-Enrich, B Amos, Y Lipman, ... International Conference on Machine Learning, ICML 2023, 2023 | 119 | 2023 |
| torchdiffeq, 2018 RTQ Chen URL https://github. com/rtqichen/torchdiffeq 14, 0 | 103 | |
| Theseus: A Library for Differentiable Nonlinear Optimization L Pineda, T Fan, M Monge, S Venkataraman, P Sodhi, R Chen, J Ortiz, ... Advances in Neural Information Processing Systems, NeurIPS 2022, 2022 | 100 | 2022 |
| Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization CW Huang, RTQ Chen, C Tsirigotis, A Courville International Conference on Learning Representations, ICLR 2021, 2021 | 98 | 2021 |
| Scalable gradients and variational inference for stochastic differential equations X Li, TKL Wong, RTQ Chen, DK Duvenaud Symposium on Advances in Approximate Bayesian Inference, 1-28, 2020 | 88 | 2020 |
| “Hey, that’s not an ODE”: Faster ODE Adjoints via Seminorms P Kidger, RTQ Chen, T Lyons International Conference on Machine Learning, ICML 2021, 2021 | 72* | 2021 |
| Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations W Xu, RTQ Chen, X Li, D Duvenaud Artificial Intelligence and Statistics, AISTATS 2022, 2022 | 62 | 2022 |
David DuvenaudAssociate Professor, University of Toronto在 cs.toronto.edu 的電子郵件地址已通過驗證