| Bayesian distributed stochastic gradient descent M Teng, F Wood Advances in Neural Information Processing Systems 31, 2018 | 21 | 2018 |
| Nonintercalating nanosubstrates create asymmetry between bilayer leaflets S Varma, M Teng, HL Scott Langmuir 28 (5), 2842-2848, 2012 | 15 | 2012 |
| Poloxamer 188 decreases membrane toxicity of mutant SOD1 and ameliorates pathology observed in SOD1 mouse model for ALS JJ Riehm, L Wang, G Ghadge, M Teng, AM Correa, JD Marks, RP Roos, ... Neurobiology of disease 115, 115-126, 2018 | 11 | 2018 |
| Near-optimal glimpse sequences for improved hard attention neural network training W Harvey, M Teng, F Wood 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 6 | 2022 |
| Semi-supervised sequential generative models M Teng, TA Le, A Scibior, F Wood arXiv preprint arXiv:2007.00155, 2020 | 6 | 2020 |
| High throughput synchronous distributed stochastic gradient descent M Teng, F Wood arXiv preprint arXiv:1803.04209, 2018 | 3 | 2018 |
| A closer look at gradient estimators with reinforcement learning as inference JW Lavington, M Teng, M Schmidt, F Wood Deep RL Workshop NeurIPS 2021, 2021 | 2 | 2021 |
| Exploration with multi-sample target values for distributional reinforcement learning M Teng, M van de Panne, F Wood arXiv preprint arXiv:2202.02693, 2022 | 1 | 2022 |
| Imitation learning of factored multi-agent reactive models M Teng, TA Le, A Scibior, F Wood arXiv preprint arXiv:1903.04714, 2019 | 1 | 2019 |
| Applications of time-series generative models and inference techniques M Teng University of Oxford, 2022 | | 2022 |
| Near-Optimal Glimpse Sequences for Training Hard Attention Neural Networks W Harvey, M Teng, F Wood | | |
Frank WoodUniversity of British Columbia在 cs.ubc.ca 的电子邮件经过验证
