| Neural Ordinary Differential Equations RTQ Chen, Y Rubanova, J Bettencourt, D Duvenaud Neural Information Processing Systems, 2018 | 8421 | 2018 |
| Convolutional Networks on Graphs for Learning Molecular Fingerprints D Duvenaud, D Maclaurin, J Aguilera-Iparraguirre, R Gómez-Bombarelli, ... Neural Information Processing Systems, 2015 | 5036 | 2015 |
| Automatic chemical design using a data-driven continuous representation of molecules R Gómez-Bombarelli, JN Wei, D Duvenaud, JM Hernández-Lobato, ... ACS central science 4 (2), 268-276, 2018 | 4477 | 2018 |
| Isolating sources of disentanglement in variational autoencoders RTQ Chen, X Li, R Grosse, D Duvenaud Neural Information Processing Systems, arXiv preprint arXiv:1802.04942, 2018 | 1802 | 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, 2018 | 1334* | 2018 |
| Latent ODEs for irregularly-sampled time series Y Rubanova, RTQ Chen, D Duvenaud Neural Information Processing Systems, 2019 | 1326* | 2019 |
| Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International conference on machine learning, 2113-2122, 2015 | 1267 | 2015 |
| Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach R Gómez-Bombarelli, J Aguilera-Iparraguirre, TD Hirzel, D Duvenaud, ... Nature materials 15 (10), 1120-1127, 2016 | 1174 | 2016 |
| Automatic model construction with Gaussian processes D Duvenaud | 1135 | 2014 |
| Invertible residual networks J Behrmann, W Grathwohl, RTQ Chen, D Duvenaud, JH Jacobsen International Conference on Machine Learning, 2018 | 839 | 2018 |
| Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One W Grathwohl, KC Wang, JH Jacobsen, D Duvenaud, M Norouzi, ... International Conference on Learning Representations 2020, 2019 | 776 | 2019 |
| Structure Discovery in Nonparametric Regression through Compositional Kernel Search D Duvenaud, JR Lloyd, R Grosse, JB Tenenbaum, Z Ghahramani International Conference on Machine Learning, 2013 | 758 | 2013 |
| Composing graphical models with neural networks for structured representations and fast inference MJ Johnson, DK Duvenaud, A Wiltschko, RP Adams, SR Datta Advances in neural information processing systems 29, 2016 | 615 | 2016 |
| Towards understanding sycophancy in language models M Sharma, M Tong, T Korbak, D Duvenaud, A Askell, SR Bowman, ... arXiv preprint arXiv:2310.13548, 2023 | 592 | 2023 |
| Optimizing Millions of Hyperparameters by Implicit Differentiation J Lorraine, P Vicol, D Duvenaud Artificial Intelligence and Statistics, 2019 | 576 | 2019 |
| Scalable Gradients for Stochastic Differential Equations X Li, TKL Wong, RTQ Chen, D Duvenaud Artificial Intelligence and Statistics, 2020 | 571* | 2020 |
| Neural networks for the prediction of organic chemistry reactions JN Wei, D Duvenaud, A Aspuru-Guzik ACS central science 2 (10), 725-732, 2016 | 526 | 2016 |
| Additive Gaussian Processes D Duvenaud, H Nickisch, CE Rasmussen Neural Information Processing Systems, 2011 | 514 | 2011 |
| Residual Flows for Invertible Generative Modeling RTQ Chen, J Behrmann, D Duvenaud, JH Jacobsen Neural Information Processing Systems, 2019 | 508 | 2019 |
| Efficient Graph Generation with Graph Recurrent Attention Networks R Liao, Y Li, Y Song, S Wang, C Nash, WL Hamilton, D Duvenaud, ... Neural Information Processing Systems, 2019 | 480 | 2019 |
Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
