| Neural ordinary differential equations RTQ Chen, Y Rubanova, J Bettencourt, DK Duvenaud Advances in neural information processing systems, 6571-6583, 2018 | 4026 | 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 | 1212 | 2018 |
| 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 | 730 | 2019 |
| Latent odes for irregularly-sampled time series Y Rubanova, RTQ Chen, D Duvenaud Advances in Neural Information Processing Systems, NeurIPS 2019, 2019 | 703* | 2019 |
| Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International Conference on Machine Learning, ICML 2019, 2019 | 543 | 2019 |
| Fast patch-based style transfer of arbitrary style RTQ Chen, M Schmidt Constructive Machine Learning Workshop, NIPS 2016, 2016 | 396 | 2016 |
| Residual flows for invertible generative modeling RTQ Chen, J Behrmann, DK Duvenaud, JH Jacobsen Advances in Neural Information Processing Systems, 9913-9923, 2019 | 330 | 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 | 251 | 2020 |
| Scalable reversible generative models with free-form continuous dynamics W Grathwohl, RTQ Chen, J Bettencourt, D Duvenaud International Conference on Learning Representations, 2019 | 105 | 2019 |
| Learning Neural Event Functions for Ordinary Differential Equations RTQ Chen, B Amos, M Nickel International Conference on Learning Representations, ICLR 2021, 2021 | 99 | 2021 |
| Flow Matching for Generative Modeling Y Lipman, RTQ Chen, H Ben-Hamu, M Nickel, M Le International Conference on Learning Representations, ICLR 2023, 2022 | 95 | 2022 |
| Neural Spatio-Temporal Point Processes RTQ Chen, B Amos, M Nickel International Conference on Learning Representations, ICLR 2021, 2021 | 71 | 2021 |
| 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 | 69 | 2021 |
| 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 | 50 | 2022 |
| 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 | 47 | 2020 |
| torchdiffeq, 2018 RTQ Chen URL https://github. com/rtqichen/torchdiffeq, 0 | 45 | |
| “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 | 41* | 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 | 38 | 2022 |
| Neural networks with cheap differential operators RTQ Chen, D Duvenaud Advances in Neural Information Processing Systems, 9961-9971, 2019 | 31 | 2019 |
| SUMO: Unbiased estimation of log marginal probability for latent variable models Y Luo, A Beatson, M Norouzi, J Zhu, D Duvenaud, RP Adams, RTQ Chen International Conference on Learning Representations, ICLR 2020, 2020 | 21 | 2020 |
David DuvenaudAssociate Professor, University of TorontoVerified email at cs.toronto.edu
