Modern Machine Learning and Particle Physics by Matthew D. Schwartz

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Jet charge and machine learning by Katherine Fraser, Matthew D. Schwartz

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JUNIPR: a framework for unsupervised machine learning in particle physics by Anders Andreassen, Ilya Feige, Christopher Frye, Matthew D. Schwartz

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Prospects for strong coupling measurement at hadron colliders using soft-drop jet mass by Holmfridur S. Hannesdottir, Aditya Pathak, Matthew D. Schwartz, Iain W. Stewart

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Neural network field theories: non-Gaussianity, actions, and locality by Mehmet Demirtas, James Halverson, Anindita Maiti, Matthew D Schwartz, Keegan Stoner

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Constraints on sequential discontinuities from the geometry of on-shell spaces by Holmfridur S. Hannesdottir, Andrew J. McLeod, Matthew D. Schwartz, Cristian Vergu

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Parameter inference from event ensembles and the top-quark mass by Forrest Flesher, Katherine Fraser, Charles Hutchison, Bryan Ostdiek, Matthew D. Schwartz

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NNLL resummation of Sudakov shoulder logarithms in the heavy jet mass distribution by Arindam Bhattacharya, Johannes K. L. Michel, Matthew D. Schwartz, Iain W. Stewart, Xiaoyuan Zhang

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Sequential discontinuities of Feynman integrals and the monodromy group by Jacob L. Bourjaily, Holmfridur Hannesdottir, Andrew J. McLeod, Matthew D. Schwartz, Cristian Vergu

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