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Machine Learning Algebraic Geometry for Physics


Apr 21, 2022
Jiakang Bao, Yang-Hui He, Elli Heyes, Edward Hirst

* 32 pages, 25 figures. Contribution to Machine learning and Algebraic Geometry, edited by A. Kasprzyk et al 

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Murmurations of elliptic curves


Apr 21, 2022
Yang-Hui He, Kyu-Hwan Lee, Thomas Oliver, Alexey Pozdnyakov

* 25 pages, 16 figures, 2 tables 

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Cluster Algebras: Network Science and Machine Learning


Mar 25, 2022
Pierre-Philippe Dechant, Yang-Hui He, Elli Heyes, Edward Hirst

* 38 pages, 27 figures 

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From the String Landscape to the Mathematical Landscape: a Machine-Learning Outlook


Feb 12, 2022
Yang-Hui He

* 10 pages, 2 figures. Based on various talks in 2021-22, this is an invited contribution to the Proceedings of "The 14th International Workshop on Lie theory and its applications in physics", to be published by Springer-Nature 

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Machine-Learning the Classification of Spacetimes


Jan 05, 2022
Yang-Hui He, Juan Manuel Pérez Ipiña

* 6 pages, 5 figures 

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Calabi-Yau Metrics, Energy Functionals and Machine-Learning


Dec 20, 2021
Anthony Ashmore, Lucille Calmon, Yang-Hui He, Burt A. Ovrut

* 7 pages, 5 figures 

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Machine Learning Calabi-Yau Hypersurfaces


Dec 12, 2021
David S. Berman, Yang-Hui He, Edward Hirst

* 32 pages, 45 figures 

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The World in a Grain of Sand: Condensing the String Vacuum Degeneracy


Nov 08, 2021
Yang-Hui He, Shailesh Lal, M. Zaid Zaz

* 12 pages, 15 figures, 3 tables 

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Neurons on Amoebae


Jun 07, 2021
Jiakang Bao, Yang-Hui He, Edward Hirst

* 50 pages 

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Machine-Learning Mathematical Structures


Jan 15, 2021
Yang-Hui He

* 32 pages. Based on various colloquia, seminars and conference talks in 2020, this is a contribution to the launch of the journal "Data Science in the Mathematical Sciences." 

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Machine-Learning Arithmetic Curves


Dec 07, 2020
Yang-Hui He, Kyu-Hwan Lee, Thomas Oliver

* 21 pages, 1 figure, 9 tables 

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Machine-Learning Number Fields


Nov 17, 2020
Yang-Hui He, Kyu-Hwan Lee, Thomas Oliver

* 20 pages, 1 figure, 3 tables 

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Machine Learning Lie Structures & Applications to Physics


Nov 02, 2020
Heng-Yu Chen, Yang-Hui He, Shailesh Lal, Suvajit Majumder

* 6 pages, 6 figures 

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Machine-Learning the Sato--Tate Conjecture


Oct 30, 2020
Yang-Hui He, Kyu-Hwan Lee, Thomas Oliver

* 21 pages, 1 table, 5 figures 

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Machine Learning Calabi-Yau Four-folds


Sep 14, 2020
Yang-Hui He, Andre Lukas

* 6 pages, 2 figures; references added 

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Graph Laplacians, Riemannian Manifolds and their Machine-Learning


Jun 30, 2020
Yang-Hui He, Shing-Tung Yau

* 47+1 pages, 30 figures 

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Machine-Learning Dessins d'Enfants: Explorations via Modular and Seiberg-Witten Curves


Apr 22, 2020
Yang-Hui He, Edward Hirst, Toby Peterken

* 52 pages, 197 figures 

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Machine Learning String Standard Models


Mar 30, 2020
Rehan Deen, Yang-Hui He, Seung-Joo Lee, Andre Lukas

* CERN-TH-2020-050, CTPU-PTC-20-06 
* 10 pages 

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Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer


Nov 04, 2019
Laura Alessandretti, Andrea Baronchelli, Yang-Hui He

* 40 pages, 19 figures 

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Machine learning Calabi-Yau metrics


Oct 18, 2019
Anthony Ashmore, Yang-Hui He, Burt Ovrut

* 57 pages, 14 figures 

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Learning Algebraic Structures: Preliminary Investigations


May 02, 2019
Yang-Hui He, Minhyong Kim

* 26 pages, 4 figures 

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Machine-learning a virus assembly fitness landscape


Jan 13, 2019
Pierre-Philippe Dechant, Yang-Hui He

* 13 pages, 4 figures 

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The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning


Dec 07, 2018
Yang-Hui He

* #pages = #irreps(Monster), 44 figures, book to appear with Springer 

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Machine Learning CICY Threefolds


Jul 31, 2018
Kieran Bull, Yang-Hui He, Vishnu Jejjala, Challenger Mishra

* 22 pages, 10 figures. V2: Added new results of using permutations of the CICY matrix to reduce the class imbalance when predicting discrete symmetries. V3: Corrected typing errors in table 5 

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hep-th


Jun 27, 2018
Yang-Hui He, Vishnu Jejjala, Brent D. Nelson

* 50 pages, 6 figures 

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Deep-Learning the Landscape


Jan 27, 2018
Yang-Hui He

* 35 pages, 4 figures, code available, refs and comments added on v2, substantial updates on training curves and validation on v3 

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