Stebėti
Brooks Paige
Brooks Paige
Associate Professor, University College London
Patvirtintas el. paštas ucl.ac.uk - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
Grammar variational autoencoder
MJ Kusner, B Paige, JM Hernández-Lobato
Proceedings of the 34th International Conference on Machine Learning, 1945-1954, 2017
11392017
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
411*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
3032019
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
2562019
An introduction to probabilistic programming
JW van de Meent, B Paige, H Yang, F Wood
arXiv preprint arXiv:1809.10756, 2018
2142018
Structured Disentangled Representations
B Esmaeili, H Wu, S Jain, A Bozkurt, N Siddharth, B Paige, DH Brooks, ...
arXiv preprint arXiv:1804.02086, 2018
208*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
2002021
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
1262019
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
1242021
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
1172016
A compilation target for probabilistic programming languages
B Paige, F Wood
Proceedings of The 31st International Conference on Machine Learning, 1935--1943, 2014
872014
A generative model for electron paths
J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato
International Conference on Learning Representations (ICLR), 2019
85*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
672020
Asynchronous anytime sequential monte carlo
B Paige, F Wood, A Doucet, YW Teh
Advances in neural information processing systems 27, 2014
592014
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
532013
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
442020
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
412016
Black-box policy search with probabilistic programs
JW Vandemeent, B Paige, D Tolpin, F Wood
Artificial Intelligence and Statistics, 1195-1204, 2016
302016
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
252018
I don't need u: Identifiable non-linear ica without side information
M Willetts, B Paige
arXiv preprint arXiv:2106.05238, 2021
212021
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Straipsniai 1–20