# 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…

## 6 Citations

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