| Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato Proceedings of the 34th International Conference on Machine Learning, 1945-1954, 2017 | 1138 | 2017 |
| Learning disentangled representations with semi-supervised deep generative models N Siddharth, B Paige, JW Van de Meent, A Desmaison, F Wood, ... Advances in Neural Information Processing Systems (NIPS) 30, 5925–5935, 2017 | 411* | 2017 |
| Variational mixture-of-experts autoencoders for multi-modal deep generative models Y Shi, B Paige, P Torr Advances in neural information processing systems 32, 2019 | 303 | 2019 |
| Take a look around: using street view and satellite images to estimate house prices S Law, B Paige, C Russell ACM Transactions on Intelligent Systems and Technology (TIST) 10 (5), 1-19, 2019 | 256 | 2019 |
| An introduction to probabilistic programming JW van de Meent, B Paige, H Yang, F Wood arXiv preprint arXiv:1809.10756, 2018 | 214 | 2018 |
| Structured Disentangled Representations B Esmaeili, H Wu, S Jain, A Bozkurt, N Siddharth, B Paige, DH Brooks, ... arXiv preprint arXiv:1804.02086, 2018 | 207 | 2018 |
| Seasonal Arctic sea ice forecasting with probabilistic deep learning TR Andersson, JS Hosking, M Pérez-Ortiz, B Paige, A Elliott, C Russell, ... Nature communications 12 (1), 5124, 2021 | 200 | 2021 |
| A model to search for synthesizable molecules J Bradshaw, B Paige, MJ Kusner, M Segler, JM Hernández-Lobato Advances in Neural Information Processing Systems 32, 2019 | 126 | 2019 |
| Simulation intelligence: Towards a new generation of scientific methods A Lavin, D Krakauer, H Zenil, J Gottschlich, T Mattson, J Brehmer, ... arXiv preprint arXiv:2112.03235, 2021 | 124 | 2021 |
| Inference networks for sequential Monte Carlo in graphical models B Paige, F Wood Proceedings of the 33rd International Conference on Machine Learning, 3040-3049, 2016 | 117 | 2016 |
| A compilation target for probabilistic programming languages B Paige, F Wood Proceedings of The 31st International Conference on Machine Learning, 1935--1943, 2014 | 87 | 2014 |
| A generative model for electron paths J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato International Conference on Learning Representations (ICLR), 2019 | 84* | 2019 |
| Barking up the right tree: an approach to search over molecule synthesis dags J Bradshaw, B Paige, MJ Kusner, M Segler, JM Hernández-Lobato Advances in neural information processing systems 33, 6852-6866, 2020 | 67 | 2020 |
| Asynchronous anytime sequential monte carlo B Paige, F Wood, A Doucet, YW Teh Advances in neural information processing systems 27, 2014 | 59 | 2014 |
| Bayesian inference and online experimental design for mapping neural microcircuits B Shababo, B Paige, A Pakman, L Paninski Advances in Neural Information Processing Systems 26, 2013 | 53 | 2013 |
| Relating by contrasting: A data-efficient framework for multimodal generative models Y Shi, B Paige, PHS Torr, N Siddharth arXiv preprint arXiv:2007.01179, 2020 | 44 | 2020 |
| Interacting particle markov chain monte carlo T Rainforth, C Naesseth, F Lindsten, B Paige, JW Vandemeent, A Doucet, ... International Conference on Machine Learning, 2616-2625, 2016 | 41 | 2016 |
| Black-box policy search with probabilistic programs JW Vandemeent, B Paige, D Tolpin, F Wood Artificial Intelligence and Statistics, 1195-1204, 2016 | 30 | 2016 |
| Learning a Generative Model for Validity in Complex Discrete Structures D Janz, J van der Westhuizen, B Paige, MJ Kusner, ... International Conference on Learning Representations (ICLR), 2018 | 25 | 2018 |
| I don't need u: Identifiable non-linear ica without side information M Willetts, B Paige arXiv preprint arXiv:2106.05238, 2021 | 21 | 2021 |
Frank WoodUniversity of British Columbia確認したメール アドレス: cs.ubc.ca
