text: 186,115,1423486044457025536,917589174,Harold Erbin,"New paper with @thesfinox, Robin Schneider and Mohamed Tamaazousti: we compute Hodge numbers for complete intersection #CalabiYau using #DeepLearning. Accuracy for 80% training ratio: 100% for h(1,1) and h(2,1), 96% for h(3,1), 83% for h(2,2) <LINK> @thesfinox And it's a new milestone since it's my 30th paper!",https://arxiv.org/abs/2108.02221,"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 architecture. With 30% (80%) training ratio, we reach an accuracy of 100% for $h^{(1,1)}$ and 97% for $h^{(2,1)}$ (100% for both), 81% (96%) for $h^{(3,1)}$, and 49% (83%) for $h^{(2,2)}$. Assuming that the Euler number is known, as it is easy to compute, and taking into account the linear constraint arising from index computations, we get 100% total accuracy. ",Deep multi-task mining Calabi-Yau four-folds,2,"['New paper with @thesfinox, Robin Schneider and Mohamed Tamaazousti: we compute Hodge numbers for complete intersection #CalabiYau using #DeepLearning.\nAccuracy for 80% training ratio: 100% for h(1,1) and h(2,1), 96% for h(3,1), 83% for h(2,2)\n<LINK>', ""@thesfinox And it's a new milestone since it's my 30th paper!""]",21,08,311