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FAIR AI Models in High Energy Physics


Dec 21, 2022
Javier Duarte, Haoyang Li, Avik Roy, Ruike Zhu, E. A. Huerta, Daniel Diaz, Philip Harris, Raghav Kansal, Daniel S. Katz, Ishaan H. Kavoori, Volodymyr V. Kindratenko, Farouk Mokhtar, Mark S. Neubauer, Sang Eon Park, Melissa Quinnan, Roger Rusack, Zhizhen Zhao

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* 32 pages, 8 figures, 9 tables 

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Interpretability of an Interaction Network for identifying $H \rightarrow b\bar{b}$ jets


Nov 23, 2022
Avik Roy, Mark S. Neubauer

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* Contribution to Proceedings of 41st International Conference on High Energy Physics - ICHEP2022 

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A Detailed Study of Interpretability of Deep Neural Network based Top Taggers


Oct 09, 2022
Ayush Khot, Mark S. Neubauer, Avik Roy

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* Repository: https://github.com/FAIR4HEP/xAI4toptagger 

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Data Science and Machine Learning in Education


Jul 19, 2022
Gabriele Benelli, Thomas Y. Chen, Javier Duarte, Matthew Feickert, Matthew Graham, Lindsey Gray, Dan Hackett, Phil Harris, Shih-Chieh Hsu, Gregor Kasieczka, Elham E. Khoda, Matthias Komm, Mia Liu, Mark S. Neubauer, Scarlet Norberg, Alexx Perloff, Marcel Rieger, Claire Savard, Kazuhiro Terao, Savannah Thais, Avik Roy, Jean-Roch Vlimant, Grigorios Chachamis

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* Contribution to Snowmass 2021 

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Explainable AI for High Energy Physics


Jun 14, 2022
Mark S. Neubauer, Avik Roy

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* Contribution to Snowmass 2021 

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Robust Learning of Physics Informed Neural Networks


Oct 26, 2021
Chandrajit Bajaj, Luke McLennan, Timothy Andeen, Avik Roy

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Invariance-based Multi-Clustering of Latent Space Embeddings for Equivariant Learning


Jul 25, 2021
Chandrajit Bajaj, Avik Roy, Haoran Zhang

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* The codebase for MCEVAE is available at https://github.com/CVC-Lab/MCE-VAE 

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