| Backpropagation through the void: Optimizing control variates for black-box gradient estimation W Grathwohl, D Choi, Y Wu, G Roeder, D Duvenaud arXiv preprint arXiv:1711.00123, 2017 | 311 | 2017 |
| Sticking the landing: Simple, lower-variance gradient estimators for variational inference G Roeder, Y Wu, DK Duvenaud Advances in Neural Information Processing Systems 30, 2017 | 205 | 2017 |
| On linear identifiability of learned representations G Roeder, L Metz, D Kingma International Conference on Machine Learning, 9030-9039, 2021 | 65 | 2021 |
| A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation T Xue, A Beatson, M Chiaramonte, G Roeder, JT Ash, Y Menguc, ... Soft matter 16 (32), 7524-7534, 2020 | 41 | 2020 |
| Efficient amortised bayesian inference for hierarchical and nonlinear dynamical systems G Roeder, T Meeds, P Grant, A Phillips, N Dalchau International Conference on Machine Learning, 4445-4455, 2019 | 22 | 2019 |
| Learning composable energy surrogates for pde order reduction A Beatson, J Ash, G Roeder, T Xue, RP Adams Advances in neural information processing systems 33, 338-348, 2020 | 18 | 2020 |
| Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometry FM Sbahi, AJ Martinez, S Patel, D Saberi, JH Yoo, G Roeder, G Verdon arXiv preprint arXiv:2206.04663, 2022 | 8 | 2022 |
| Modelling ordinary differential equations using a variational auto encoder E Meeds, G Roeder, N Dalchau US Patent 11,030,275, 2021 | 6 | 2021 |
| Probabilistic graphical models and tensor networks: a hybrid framework J Miller, G Roeder, TD Bradley arXiv preprint arXiv:2106.15666, 2021 | 3 | 2021 |
| More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites X Sun, G Roeder, T Xue, RP Adams, S Rusinkiewicz Proceedings of the 8th ACM Symposium on Computational Fabrication, 1-14, 2023 | 1 | 2023 |
| Generative Modelling of Quantum Processes via Quantum-Probabilistic Information Geometry S Patel, F Sbahi, A Martinez, D Saberi, J Yoo, G Roeder, G Verdon | | 2022 |
| Quantum Machine Learning with Quantum-Probabilistic Generative Models A Martinez, G Roeder, G Verdon-Akzam APS March Meeting Abstracts 2021, S32. 010, 2021 | | 2021 |
| Design Motifs for Probabilistic Generative Design G Roeder, N Killoran, W Grathwohl, D Duvenaud | | 2018 |
