Brooks Paige

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Citations	3742	3358
h-index	19	18
i10-index	31	27

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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. KusnerUniversity 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
Chris RussellAssociate Professor, University of OxfordVerified email at oii.ox.ac.uk
Stephen Lawuniversity college london / alan turing instituteVerified email at turing.ac.uk
Marwin SeglerMicrosoft Research AI4ScienceVerified email at microsoft.com
Yuge (Jimmy) ShiGoogle DeepmindVerified email at google.com
Arnaud DoucetProfessor of Statistics, University of Oxford & Research Scientist, Google DeepMindVerified email at google.com
Hongseok YangProfessor, School of Computing, KAISTVerified email at kaist.ac.kr
David TolpinOfftopiaVerified email at robots.ox.ac.uk
Tom RainforthUniversity of OxfordVerified email at stats.ox.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
Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideVerified email at adelaide.edu.au
David JanzUniversity of AlbertaVerified email at cam.ac.uk
Ari PakmanBen-Gurion UniversityVerified email at bgu.ac.il
Matthew WillettsResearch Fellow, UCLVerified email at ucl.ac.uk

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	1099	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	402*	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	286	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	247	2019
An introduction to probabilistic programming JW van de Meent, B Paige, H Yang, F Wood arXiv preprint arXiv:1809.10756, 2018	207	2018
Structured Disentangled Representations B Esmaeili, H Wu, S Jain, A Bozkurt, N Siddharth, B Paige, DH Brooks, ... arXiv preprint arXiv:1804.02086, 2018	201*	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	181	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	119	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	117	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	113	2016
A compilation target for probabilistic programming languages B Paige, F Wood Proceedings of The 31st International Conference on Machine Learning, 1935--1943, 2014	86	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	81*	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	63	2020
Asynchronous anytime sequential monte carlo B Paige, F Wood, A Doucet, YW Teh Advances in neural information processing systems 27, 2014	58	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	43	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	24	2018
I don't need u: Identifiable non-linear ica without side information M Willetts, B Paige arXiv preprint arXiv:2106.05238, 2021	19	2021

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