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Towards Healing the Blindness of Score Matching


Sep 15, 2022
Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol


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Improving VAE-based Representation Learning


May 28, 2022
Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber


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Simulation Intelligence: Towards a New Generation of Scientific Methods


Dec 06, 2021
Alexander Lavin, Hector Zenil, Brooks Paige, David Krakauer, Justin Gottschlich, Tim Mattson, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer


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Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes


Nov 08, 2021
Hugh Dance, Brooks Paige


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I Don't Need $\mathbf{u}$: Identifiable Non-Linear ICA Without Side Information


Jun 09, 2021
Matthew Willetts, Brooks Paige

* 11 pages plus appendix 

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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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