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Interpretable Uncertainty Quantification in AI for HEP


Aug 08, 2022
Thomas Y. Chen, Biprateep Dey, Aishik Ghosh, Michael Kagan, Brian Nord, Nesar Ramachandra

* Submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021) 

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Discovering the building blocks of dark matter halo density profiles with neural networks


Mar 16, 2022
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam, Davide Piras

* 12 pages, 6 figures, comments welcome 

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Learning Representation for Bayesian Optimization with Collision-free Regularization


Mar 16, 2022
Fengxue Zhang, Brian Nord, Yuxin Chen

* 28 pages, 24 figures 

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Machine Learning and Cosmology


Mar 15, 2022
Cora Dvorkin, Siddharth Mishra-Sharma, Brian Nord, V. Ashley Villar, Camille Avestruz, Keith Bechtol, Aleksandra Ćiprijanović, Andrew J. Connolly, Lehman H. Garrison, Gautham Narayan, Francisco Villaescusa-Navarro

* Contribution to Snowmass 2021. 32 pages 

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DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification


Dec 28, 2021
Aleksandra Ćiprijanović, Diana Kafkes, Gregory Snyder, F. Javier Sánchez, Gabriel Nathan Perdue, Kevin Pedro, Brian Nord, Sandeep Madireddy, Stefan M. Wild

* 19 pages, 7 figures, 5 tables, submitted to Astronomy & Computing 

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Unsupervised Resource Allocation with Graph Neural Networks


Jun 17, 2021
Miles Cranmer, Peter Melchior, Brian Nord

* Accepted to PMLR/contributed oral at NeurIPS 2020 Pre-registration Workshop. Code at https://github.com/MilesCranmer/gnn_resource_allocation 

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DeepSZ: Identification of Sunyaev-Zel'dovich Galaxy Clusters using Deep Learning


Mar 08, 2021
Zhen Lin, Nicholas Huang, Camille Avestruz, W. L. Kimmy Wu, Shubhendu Trivedi, João Caldeira, Brian Nord


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Deep learning insights into cosmological structure formation


Nov 20, 2020
Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam

* 15 pages, 6 figures, to be submitted to Nature Communications, comments welcome 

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