| Deep variational reinforcement learning for POMDPs M Igl, L Zintgraf, TA Le, F Wood, S Whiteson International Conference on Machine Learning, 2117-2126, 2018 | 315 | 2018 |
| Tighter Variational Bounds are Not Necessarily Better T Rainforth, AR Kosiorek, TA Le, CJ Maddison, M Igl, F Wood, YW Teh International Conference on Machine Learning, 2018 | 225 | 2018 |
| Auto-Encoding Sequential Monte Carlo TA Le, M Igl, T Rainforth, T Jin, F Wood International Conference on Learning Representations, 2018 | 186 | 2018 |
| Inference Compilation and Universal Probabilistic Programming TA Le, AG Baydin, F Wood 20th International Conference on Artificial Intelligence and Statistics 54 …, 2017 | 167 | 2017 |
| Using Synthetic Data to Train Neural Networks is Model-Based Reasoning TA Le, AG Baydin, R Zinkov, F Wood 30th International Joint Conference on Neural Networks, 3514--3521, 2017 | 139 | 2017 |
| Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow TA Le, AR Kosiorek, N Siddharth, YW Teh, F Wood Proc. of the Conf. on Uncertainty in AI (UAI), 2019 | 75* | 2019 |
| The Thermodynamic Variational Objective V Masrani, TA Le, F Wood Advances in Neural Information Processing Systems, 11525-11534, 2019 | 57 | 2019 |
| Empirical Evaluation of Neural Process Objectives TA Le, H Kim, M Garnelo, D Rosenbaum, J Schwarz, YW Teh | 48 | 2018 |
| Bayesian optimization for probabilistic programs T Rainforth, TA Le, JW van de Meent, MA Osborne, F Wood Advances In Neural Information Processing Systems, 280-288, 2016 | 34 | 2016 |
| Learning to learn generative programs with Memoised Wake-Sleep LB Hewitt, TA Le, JB Tenenbaum Uncertainty in Artificial Intelligence, 2020 | 28 | 2020 |
| ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images MD Hoffman, TA Le, P Sountsov, C Suter, B Lee, VK Mansinghka, ... International Conference on Artificial Intelligence and Statistics, 10425-10444, 2023 | 11 | 2023 |
| Inference for higher order probabilistic programs TA Le Masters thesis, University of Oxford, 2015 | 10 | 2015 |
| Training chain-of-thought via latent-variable inference D Phan, MD Hoffman, S Douglas, TA Le, AT Parisi, P Sountsov, C Sutton, ... Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 9* | 2023 |
| Amortized Population Gibbs Samplers with Neural Sufficient Statistics H Wu, H Zimmermann, E Sennesh, TA Le, JW van de Meent International Conference on Machine Learning, 2020 | 7 | 2020 |
| Semi-supervised Sequential Generative Models M Teng, TA Le, A Scibior, F Wood Uncertainty in Artificial Intelligence, 2020 | 6 | 2020 |
| Drawing out of Distribution with Neuro-Symbolic Generative Models Y Liang, JB Tenenbaum, TA Le, N Siddharth Advances in Neural Information Processing Systems, 2022 | 5 | 2022 |
| Data-driven Sequential Monte Carlo in Probabilistic Programming YN Perov, TA Le, F Wood NIPS Workshop on Black Box Learning and Inference, 2015 | 5 | 2015 |
| Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface TA Le, KM Collins, L Hewitt, K Ellis, SJ Gershman, JB Tenenbaum International Conference on Learning Representations, 2022 | 4 | 2022 |
| Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators ML Casado, AG Baydin, DM Rubio, TA Le, F Wood, L Heinrich, G Louppe, ... NIPS Workshop on Deep Learning for Physical Sciences, 2017 | 4 | 2017 |
| Nested Compiled Inference for Hierarchical Reinforcement Learning TA Le, AG Baydin, F Wood NIPS Workshop on Bayesian Deep Learning, 2016 | 4 | 2016 |
Frank WoodUniversity of British ColumbiaVerified email at cs.ubc.ca
