| Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models G Tucker, A Mnih, CJ Maddison, J Lawson, J Sohl-Dickstein Advances in Neural Information Processing Systems 30, 2017 | 330 | 2017 |
| Filtering variational objectives CJ Maddison, D Lawson, G Tucker, N Heess, M Norouzi, A Mnih, ... Advances in Neural Information Processing Systems, 6573-6583, 2017 | 236 | 2017 |
| Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives G Tucker, D Lawson, S Gu, CJ Maddison International Conference on Learning Representations (ICLR) 2019, 2018 | 123 | 2018 |
| Changing model behavior at test-time using reinforcement learning A Odena, D Lawson, C Olah International Conference on Learning Representations (ICLR) Workshops 2017, 2017 | 53 | 2017 |
| Learning Hard Alignments with Variational Inference D Lawson, CC Chiu, G Tucker, C Raffel, K Swersky, N Jaitly IEEE International Conference on Acoustics, Speech and Signal Processing …, 2018 | 40 | 2018 |
| Energy-Inspired Models: Learning with Sampler-Induced Distributions D Lawson, G Tucker, B Dai, R Ranganath Advances in Neural Information Processing Systems 32 (2019), 2019 | 37 | 2019 |
| Twisted variational sequential monte carlo D Lawson, G Tucker, CA Naesseth, C Maddison, RP Adams, YW Teh Third workshop on Bayesian Deep Learning (NeurIPS), 2018 | 22 | 2018 |
| Particle Value Functions CJ Maddison, D Lawson, G Tucker, N Heess, A Doucet, A Mnih, YW Teh International Conference on Learning Representations (ICLR) Workshops 2017, 2017 | 19 | 2017 |
| The neural testbed: Evaluating joint predictions I Osband, Z Wen, SM Asghari, V Dwaracherla, X Lu, M Ibrahimi, ... Advances in Neural Information Processing Systems 35, 12554-12565, 2022 | 17 | 2022 |
| Evaluating predictive distributions: Does Bayesian deep learning work? I Osband, Z Wen, SM Asghari, X Lu, M Ibrahimi, V Dwaracherla, ... | 9 | 2021 |
| An online sequence-to-sequence model for noisy speech recognition CC Chiu, D Lawson, Y Luo, G Tucker, K Swersky, I Sutskever, N Jaitly arXiv preprint arXiv:1706.06428, 2017 | 8 | 2017 |
| SIXO: Smoothing Inference with Twisted Objectives D Lawson, A Raventós, A Warrington, S Linderman Advances in Neural Information Processing Systems (NeurIPS) 36, 2022 | 5 | 2022 |
| Training a subsampling mechanism in expectation C Raffel, D Lawson arXiv preprint arXiv:1702.06914, 2017 | 5 | 2017 |
| Alternating direction method of multipliers implementation using Apache Spark D Lawson Stanford University: Stanford, CA, USA, 2014 | 5 | 2014 |
| Recurrent neural networks for online sequence generation CC Chiu, N Jaitly, JD Lawson, GJ Tucker US Patent 11,625,572, 2023 | 3 | 2023 |
| Image captioning with attention B Rister, D Lawson IEEE Computation Conference, 2016 | 2 | 2016 |
| NAS-X: neural adaptive smoothing via twisting D Lawson, M Li, S Linderman Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
| Neural Adaptive Smoothing via Twisting MY Li, D Lawson, S Linderman Fifth Symposium on Advances in Approximate Bayesian Inference, 0 | 1 | |
| Adjusting neural network resource usage AQ Odena, JD Lawson US Patent App. 18/487,802, 2024 | | 2024 |
| Adjusting neural network resource usage AQ Odena, JD Lawson US Patent 11,790,211, 2023 | | 2023 |
George TuckerGoogle BrainVerified email at google.com
