| An algorithm for Aubert-Zelevinsky duality à la Mœglin-Waldspurger |
Representation Theory, Neural Network |
arXiv 2025 |
Code |
| Hilbert series, machine learning, and applications to physics |
Algebraic Geometry, Mathematical Physics, Neural Network |
Physics Letters B 2024 |
Code |
| Interpretable Machine Learning for Kronecker Coefficients |
Representation Theory, Neural Network, Symbolic Regression, PCA, Transformer |
arXiv 2025 |
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| Machine Learning Approaches to the Shafarevich-Tate Group of Elliptic Curves |
Number Theory, Neural Network, Decision Tree |
IJDSMS 2024 |
Code |
| Machine Learning Kreuzer–Skarke Calabi–Yau Threefolds |
Mathematical Physics, Algebraic Geometry, Neural Network |
arXiv 2025 |
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| Machine Learning Number Fields |
Number Theory, Neural Network, Random Forest |
MCGD 2022 |
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| Machine learning Sasakian G2 topology on contact Calabi-Yau 7-manifolds |
Mathematical Physics, Neural Network |
Physics Letters B 2024 |
Code |
| Machine Learning the vanishing order of rational L-functions |
Number Theory, LDA, Neural Network |
arXiv 2025 |
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| Neural network approximations for Calabi-Yau metrics |
Mathematical Physics, Neural Network |
JHEP 2022 |
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| Searching for ribbons with machine learning |
Geometry, Bayesian Optimization, RL, Neural Network |
Machine Learning Science and Technology 2025 |
Code |