| Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato Proceedings of the 34th International Conference on Machine Learning, 1945-1954, 2017 | 285 | 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 | 179 | 2017 |
| 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 | 86 | 2016 |
| Structured Disentangled Representations B Esmaeili, H Wu, S Jain, A Bozkurt, N Siddharth, B Paige, DH Brooks, ... arXiv preprint arXiv:1804.02086, 2018 | 64* | 2018 |
| A compilation target for probabilistic programming languages B Paige, F Wood Proceedings of The 31st International Conference on Machine Learning, 1935--1943, 2014 | 64 | 2014 |
| Asynchronous anytime sequential monte carlo B Paige, F Wood, A Doucet, YW Teh Advances in neural information processing systems, 3410-3418, 2014 | 44 | 2014 |
| An introduction to probabilistic programming JW van de Meent, B Paige, H Yang, F Wood arXiv preprint arXiv:1809.10756, 2018 | 41 | 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 | 33 | 2019 |
| 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 | 27 | 2016 |
| Bayesian inference and online experimental design for mapping neural microcircuits B Shababo, B Paige, A Pakman, L Paninski Advances in Neural Information Processing Systems, 1304-1312, 2013 | 27 | 2013 |
| A generative model for electron paths J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato International Conference on Learning Representations (ICLR), 2019 | 23* | 2019 |
| Black-box policy search with probabilistic programs JW Vandemeent, B Paige, D Tolpin, F Wood Artificial Intelligence and Statistics, 1195-1204, 2016 | 22 | 2016 |
| A model to search for synthesizable molecules J Bradshaw, B Paige, MJ Kusner, M Segler, JM Hernández-Lobato Advances in Neural Information Processing Systems, 7937-7949, 2019 | 14 | 2019 |
| 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 | 13 | 2018 |
| Variational mixture-of-experts autoencoders for multi-modal deep generative models Y Shi, N Siddharth, B Paige, P Torr Advances in Neural Information Processing Systems, 15718-15729, 2019 | 11 | 2019 |
| 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 | 6 | 2015 |
| Tempering by subsampling JW van de Meent, B Paige, F Wood arXiv preprint arXiv:1401.7145, 2014 | 6 | 2014 |
| Grammar Variational Autoencoder, 2017 MJ Kusner, B Paige, JM Hernández-Lobato arXiv preprint arXiv:1703.01925, 0 | 5 | |
| Kernel Sequential Monte Carlo I Schuster, H Strathmann, B Paige, D Sejdinovic Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017 | 4 | 2017 |
Frank WoodUniversity of British ColumbiaVerified email at cs.ubc.ca
