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Barking up the right tree: an approach to search over molecule synthesis DAGs


Dec 21, 2020
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato

* To appear in Advances in Neural Information Processing Systems 2020 

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Bayesian Graph Neural Networks for Molecular Property Prediction


Nov 25, 2020
George Lamb, Brooks Paige

* Presented at NeurIPS 2020 Machine Learning for Molecules workshop 

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Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning


Nov 24, 2020
Aneesh Pappu, Brooks Paige


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Goal-directed Generation of Discrete Structures with Conditional Generative Models


Oct 23, 2020
Amina Mollaysa, Brooks Paige, Alexandros Kalousis


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Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models


Jul 02, 2020
Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth


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Learning Bijective Feature Maps for Linear ICA


Feb 19, 2020
Alexander Camuto, Matthew Willetts, Brooks Paige, Chris Holmes, Stephen Roberts

* 8 pages 

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Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models


Nov 08, 2019
Yuge Shi, N. Siddharth, Brooks Paige, Philip H. S. Torr


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Data Generation for Neural Programming by Example


Nov 06, 2019
Judith Clymo, Haik Manukian, Nathanaël Fijalkow, Adrià Gascón, Brooks Paige


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A Model to Search for Synthesizable Molecules


Jun 12, 2019
John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin H. S. Segler, José Miguel Hernández-Lobato


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Learning a Generative Model for Validity in Complex Discrete Structures


Nov 02, 2018
David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato

* Conference paper at ICLR 2018. Code available online 

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An Introduction to Probabilistic Programming


Sep 27, 2018
Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood

* Under review at Foundations and Trends in Machine Learning 

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Take a Look Around: Using Street View and Satellite Images to Estimate House Prices


Jul 18, 2018
Stephen Law, Brooks Paige, Chris Russell

* 9 pages, 9 figures, workshop paper 

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Structured Disentangled Representations


May 29, 2018
Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer Dy, Jan-Willem van de Meent


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Predicting Electron Paths


May 23, 2018
John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato


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Inference Networks for Sequential Monte Carlo in Graphical Models


Mar 07, 2018
Brooks Paige, Frank Wood

* Paige, B., & Wood, F. (2016). Inference Networks for Sequential Monte Carlo in Graphical Models. In Proceedings of the 33rd International Conference on Machine Learning, JMLR W&CP 48: 3040-3049 
* 10 pages. Updated from version at ICML 2016; includes code at http://github.com/tbrx/compiled-inference 

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Learning Disentangled Representations with Semi-Supervised Deep Generative Models


Nov 13, 2017
N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank Wood, Philip H. S. Torr

* Accepted for publication at NIPS 2017 

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Kernel Sequential Monte Carlo


Jul 25, 2017
Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic

* ECML-PKDD 2017 

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Interacting Particle Markov Chain Monte Carlo


Apr 12, 2017
Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank Wood

* JMLR W&CP 48 : 2616-2625, 2016 

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Grammar Variational Autoencoder


Mar 06, 2017
Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato


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Inducing Interpretable Representations with Variational Autoencoders


Nov 22, 2016
N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem Van de Meent, Frank Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr

* Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems 

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Probabilistic structure discovery in time series data


Nov 21, 2016
David Janz, Brooks Paige, Tom Rainforth, Jan-Willem van de Meent, Frank Wood


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Black-Box Policy Search with Probabilistic Programs


Aug 04, 2016
Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank Wood

* Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (2016) 1195-1204 

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Path Finding under Uncertainty through Probabilistic Inference


Jun 08, 2015
David Tolpin, Brooks Paige, Jan Willem van de Meent, Frank Wood


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Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs


May 05, 2015
David Tolpin, Jan Willem van de Meent, Brooks Paige, Frank Wood


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A Compilation Target for Probabilistic Programming Languages


Jul 10, 2014
Brooks Paige, Frank Wood

* JMLR W&CP 32 (1) : 1935-1943, 2014 
* In Proceedings of the 31st International Conference on Machine Learning (ICML), 2014 

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Asynchronous Anytime Sequential Monte Carlo


Jul 10, 2014
Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh


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Tempering by Subsampling


Jan 28, 2014
Jan-Willem van de Meent, Brooks Paige, Frank Wood

* 9 pages, 3 figures, 2 tables 

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