| Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato Proceedings of the 34th International Conference on Machine Learning, 1945-1954, 2017 | 423 | 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 | 233 | 2017 |
| Structured Disentangled Representations B Esmaeili, H Wu, S Jain, A Bozkurt, N Siddharth, B Paige, DH Brooks, ... arXiv preprint arXiv:1804.02086, 2018 | 92* | 2018 |
| 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 | 89 | 2016 |
| A compilation target for probabilistic programming languages B Paige, F Wood Proceedings of The 31st International Conference on Machine Learning, 1935--1943, 2014 | 69 | 2014 |
| An introduction to probabilistic programming JW van de Meent, B Paige, H Yang, F Wood arXiv preprint arXiv:1809.10756, 2018 | 66 | 2018 |
| 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 | 63 | 2019 |
| Asynchronous anytime sequential monte carlo B Paige, F Wood, A Doucet, YW Teh arXiv preprint arXiv:1407.2864, 2014 | 52 | 2014 |
| Variational mixture-of-experts autoencoders for multi-modal deep generative models Y Shi, N Siddharth, B Paige, PHS Torr arXiv preprint arXiv:1911.03393, 2019 | 45 | 2019 |
| Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. B Shababo, B Paige, A Pakman, L Paninski NIPS 26, 1304-1312, 2013 | 37 | 2013 |
| A model to search for synthesizable molecules J Bradshaw, B Paige, MJ Kusner, MHS Segler, JM Hernández-Lobato arXiv preprint arXiv:1906.05221, 2019 | 35 | 2019 |
| A generative model for electron paths J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato International Conference on Learning Representations (ICLR), 2019 | 35* | 2019 |
| Black-box policy search with probabilistic programs JW Vandemeent, B Paige, D Tolpin, F Wood Artificial Intelligence and Statistics, 1195-1204, 2016 | 29 | 2016 |
| 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 | 28 | 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 | 15 | 2018 |
| Inducing interpretable representations with variational autoencoders N Siddharth, B Paige, A Desmaison, JW Van de Meent, F Wood, ... arXiv preprint arXiv:1611.07492, 2016 | 8 | 2016 |
| Output-sensitive adaptive metropolis-hastings for probabilistic programs D Tolpin, JW van de Meent, B Paige, F Wood Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015 | 7 | 2015 |
| Kernel sequential monte carlo I Schuster, H Strathmann, B Paige, D Sejdinovic Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017 | 6 | 2017 |
| Tempering by subsampling JW van de Meent, B Paige, F Wood arXiv preprint arXiv:1401.7145, 2014 | 6 | 2014 |
| Probabilistic structure discovery in time series data D Janz, B Paige, T Rainforth, JW van de Meent, F Wood arXiv preprint arXiv:1611.06863, 2016 | 5 | 2016 |
Frank WoodUniversity of British ColumbiaVerified email at cs.ubc.ca
