Brooks Paige
Brooks Paige
Associate Professor, University College London
Verified email at ucl.ac.uk - Homepage
Title
Cited by
Cited by
Year
Grammar variational autoencoder
MJ Kusner, B Paige, JM Hernández-Lobato
Proceedings of the 34th International Conference on Machine Learning, 1945-1954, 2017
3942017
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
2232017
Structured Disentangled Representations
B Esmaeili, H Wu, S Jain, A Bozkurt, N Siddharth, B Paige, DH Brooks, ...
arXiv preprint arXiv:1804.02086, 2018
88*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
882016
A compilation target for probabilistic programming languages
B Paige, F Wood
Proceedings of The 31st International Conference on Machine Learning, 1935--1943, 2014
682014
An introduction to probabilistic programming
JW van de Meent, B Paige, H Yang, F Wood
arXiv preprint arXiv:1809.10756, 2018
632018
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
542019
Asynchronous anytime sequential monte carlo
B Paige, F Wood, A Doucet, YW Teh
arXiv preprint arXiv:1407.2864, 2014
522014
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits.
B Shababo, B Paige, A Pakman, L Paninski
NIPS 26, 1304-1312, 2013
352013
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
342019
A generative model for electron paths
J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato
International Conference on Learning Representations (ICLR), 2019
33*2019
A model to search for synthesizable molecules
J Bradshaw, B Paige, MJ Kusner, MHS Segler, JM Hernández-Lobato
arXiv preprint arXiv:1906.05221, 2019
312019
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
282016
Black-box policy search with probabilistic programs
JW Vandemeent, B Paige, D Tolpin, F Wood
Artificial Intelligence and Statistics, 1195-1204, 2016
282016
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
152018
Inducing interpretable representations with variational autoencoders
N Siddharth, B Paige, A Desmaison, JW Van de Meent, F Wood, ...
arXiv preprint arXiv:1611.07492, 2016
82016
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
72015
Kernel sequential monte carlo
I Schuster, H Strathmann, B Paige, D Sejdinovic
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017
62017
Tempering by subsampling
JW van de Meent, B Paige, F Wood
arXiv preprint arXiv:1401.7145, 2014
62014
Super-sampling with a reservoir.
B Paige, D Sejdinovic, FD Wood
UAI, 2016
52016
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Articles 1–20