Deep multi-task mining Calabi–Yau four-folds

@article{Erbin2022DeepMM,
  title={Deep multi-task mining Calabi–Yau four-folds},
  author={Harold Erbin and Riccardo Finotello and Robin Schneider and Mohamed Tamaazousti},
  journal={Machine Learning: Science and Technology},
  year={2022},
  volume={3}
}
We continue earlier efforts in computing the dimensions of tangent space cohomologies of Calabi–Yau manifolds using deep learning. In this paper, we consider the dataset of all Calabi–Yau four-folds constructed as complete intersections in products of projective spaces. Employing neural networks inspired by state-of-the-art computer vision architectures, we improve earlier benchmarks and demonstrate that all four non-trivial Hodge numbers can be learned at the same time using a multi-task… 

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