Brooks Paige

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Citations	2513	2337
h-index	19	18
i10-index	24	21

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Frank WoodUniversity of British ColumbiaVerified email at cs.ubc.ca
Jan-Willem van de MeentUniversity of Amsterdam and Northeastern UniversityVerified email at uva.nl
Matt J. KusnerAssociate Professor, University College LondonVerified email at ucl.ac.uk
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Philip TorrProfessor, University of OxfordVerified email at eng.ox.ac.uk
N. SiddharthReader in Explainable AI, University of EdinburghVerified email at iffsid.com
Marwin SeglerMicrosoft ResearchVerified email at microsoft.com
Chris RussellAmazonVerified email at amazon.de
Babak EsmaeiliUniversity of AmsterdamVerified email at uva.nl
Yuge ShiUniversity of OxfordVerified email at robots.ox.ac.uk
Arnaud DoucetProfessor of Statistics, University of Oxford & Senior Research Scientist, Google DeepMindVerified email at stats.ox.ac.uk
David TolpinOfftopiaVerified email at robots.ox.ac.uk
Stephen Lawuniversity college london / alan turing instituteVerified email at turing.ac.uk
Hongseok YangProfessor, School of Computing, KAISTVerified email at kaist.ac.kr
Tom RainforthFlorence Nightingale Bicentennial Fellow, University of OxfordVerified email at stats.ox.ac.uk
David JanzUniversity of AlbertaVerified email at cam.ac.uk
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Liam PaninskiColumbia UniversityVerified email at stat.columbia.edu
Ari PakmanBen-Gurion UniversityVerified email at bgu.ac.il
Christian A. NaessethAssistant Professor at University of AmsterdamVerified email at uva.nl

Brooks Paige

Associate Professor, University College London

Verified email at ucl.ac.uk - Homepage

Machine Learning Statistics


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Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato Proceedings of the 34th International Conference on Machine Learning, 1945-1954, 2017	769	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	330*	2017
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	160	2019
Structured Disentangled Representations B Esmaeili, H Wu, S Jain, A Bozkurt, N Siddharth, B Paige, DH Brooks, ... arXiv preprint arXiv:1804.02086, 2018	154*	2018
An introduction to probabilistic programming JW van de Meent, B Paige, H Yang, F Wood arXiv preprint arXiv:1809.10756, 2018	144	2018
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	139	2019
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	102	2016
A compilation target for probabilistic programming languages B Paige, F Wood Proceedings of The 31st International Conference on Machine Learning, 1935--1943, 2014	80	2014
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	77	2019
Asynchronous anytime sequential monte carlo B Paige, F Wood, A Doucet, YW Teh Advances in neural information processing systems 27, 2014	56	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	54*	2019
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	47	2021
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	42	2013
Simulation intelligence: Towards a new generation of scientific methods A Lavin, H Zenil, B Paige, D Krakauer, J Gottschlich, T Mattson, ... arXiv preprint arXiv:2112.03235, 2021	39	2021
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	39	2016
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	33	2020
Black-box policy search with probabilistic programs JW Vandemeent, B Paige, D Tolpin, F Wood Artificial Intelligence and Statistics, 1195-1204, 2016	26	2016
International Conference on Machine Learning MJ Kusner, B Paige, JM Hernández-Lobato PMLR,, 2017	25	2017
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	20	2018
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	14	2020

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