| 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 | 224 | 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 | 135 | 2017 |
| On linear identifiability of learned representations G Roeder, L Metz, D Kingma International Conference on Machine Learning, 9030-9039, 2021 | 24 | 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 | 16 | 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 | 13 | 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 | 11 | 2020 |
| Probabilistic graphical models and tensor networks: A hybrid framework J Miller, G Roeder, TD Bradley arXiv preprint arXiv:2106.15666, 2021 | 2 | 2021 |
| Modelling ordinary differential equations using a variational auto encoder E Meeds, G Roeder, N Dalchau US Patent 11,030,275, 2021 | 1 | 2021 |
| Quantum Machine Learning with Quantum-Probabilistic Generative Models A Martinez, G Roeder, G Verdon-Akzam Bulletin of the American Physical Society 66, 2021 | | 2021 |
| Design Motifs for Probabilistic Generative Design G Roeder, N Killoran, W Grathwohl, D Duvenaud | | 2018 |
| Learning Composable Energy Surrogates for PDE Order Reduction Open Website A Beatson, JT Ash, G Roeder, T Xue, RP Adams | | |
