# Deep multi-task mining Calabi–Yau four-folds

@article{Erbin2022DeepMM,
author={Harold Erbin and Riccardo Finotello and Robin Schneider and Mohamed Tamaazousti},
journal={Machine Learning: Science and Technology},
year={2022},
volume={3}
}
• Published 4 August 2021
• Computer Science, Mathematics
• Machine Learning: Science and Technology
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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## References

SHOWING 1-10 OF 59 REFERENCES

### Machine learning Calabi-Yau four-folds

• Mathematics, Computer Science
Physics Letters B
• 2021

### Machine learning for complete intersection Calabi-Yau manifolds: a methodological study

• Computer Science
ArXiv
• 2020
Improved accuracy of ML computations for Hodge numbers with respect to the existing literature is obtained, serving as a proof of concept that neural networks can be valuable to study the properties of geometries appearing in string theory.

### Inception neural network for complete intersection Calabi–Yau 3-folds

• Computer Science, Mathematics
Mach. Learn. Sci. Technol.
• 2021
A neural network inspired by Google’s Inception model is introduced to compute the Hodge number h 1,1 of complete intersection Calabi–Yau (CICY) 3-folds, giving already 97% of accuracy with just 30% of the data for training.

### Machine learning CICY threefolds

• Computer Science
Physics Letters B
• 2018

### Rethinking the Inception Architecture for Computer Vision

• Computer Science
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
• 2016
This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.

### Explore and Exploit with Heterotic Line Bundle Models

• Computer Science
Fortschritte der Physik
• 2020
Deep reinforcement learning is used to explore a class of heterotic SU(5) GUT models constructed from line bundle sums over Complete Intersection Calabi Yau (CICY) manifolds and concludes that the agents detect hidden structures in the compactification data, which is partly of general nature.

### Getting CICY high

• Computer Science
Physics Letters B
• 2019

### Deep-Learning the Landscape

We propose a paradigm to deep-learn the ever-expanding databases which have emerged in mathematical physics and particle phenomenology, as diverse as the statistics of string vacua or combinatorial

### ImageNet classification with deep convolutional neural networks

• Computer Science
Commun. ACM
• 2012
A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.