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We apply some of the latest techniques from machine-learning to the arithmetic of hyperelliptic curves.
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Graph Laplacians, Riemannian Manifolds and their Machine-Learning
Y. H. He and S. T. Yau, · 2006
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Auto-correlation functions of Sato–Tate distributions and identities of symplectic characters
K.-H. Lee and S.-J. Oh, · 2006
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Automorphy for some ℓ \ell -adic lifts of automorphic mod ℓ \ell Galois Representations II
R. Taylor, · 2008
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C. Johansson. On the Sato–Tate conjecture for non-generic abelian surfaces · 2017
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Machine Learning of Calabi-Yau Volumes
D. Krefl and R. K. Seong, · 2017
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Evolving neural networks with genetic algorithms to study the String Landscape
F. Ruehle, · 2017
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Rigorous computation of the endomorphism ring of a Jacobian
E. Costa, N. Mascot, J. Sijsling, and J. Voight, · 2019
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Deep Learning the Hyperbolic Volume of a Knot
V. Jejjala, A. Kar, and O. Parrikar, · 2019
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Hyperelliptic curves, L L -polynomials, and random matrices
K. S. Kedlaya and A. V. Sutherland · 2019
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Neural Network prediction of Riemann zeta zeros
O. Shanker, · 2012
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A database of genus-2 curves over the rational numbers
A. Booker, J. Sijsling, A. Sutherland, J. Voight, and D. Yasaki, · 2016
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Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning - Adaptive Computation and Machine Learning
2016
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J. Carifio, J. Halverson, D. Krioukov, and B. D. Nelson, · 2017
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Machine-learning the string landscape
Y. H. He, · 2017
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Potential automorphy over CM fields
P. B. Allen et al
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The L-functions and Modular Forms Database
The LMFDB Collaboration, · 2020
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The Sage Development Team, · 2020
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Sato–Tate distributions on Abelian surfaces
N. Taylor, · 2020
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Wolfram Research, Inc., Mathematica 12.1
2020
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