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Found 6 papers, 3 papers with code
Deep learning complete intersection Calabi-Yau manifolds
We review advancements in deep learning techniques for complete intersection Calabi-Yau (CICY) 3- and 4-folds, with the aim of understanding better how to handle algebraic topological data with machine learning.
Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning
The procedure is an end-to-end pipeline including the process of synthetic data augmentation, the construction of a suitable robust, homoscedastic, deep learning model, and the validation of its predictions.
HyperPCA: a Powerful Tool to Extract Elemental Maps from Noisy Data Obtained in LIBS Mapping of Materials
Laser-induced breakdown spectroscopy is a preferred technique for fast and direct multi-elemental mapping of samples under ambient pressure, without any limitation on the targeted element.
Deep multi-task mining Calabi-Yau four-folds
We continue earlier efforts in computing the dimensions of tangent space cohomologies of Calabi-Yau manifolds using deep learning.
Machine learning for complete intersection Calabi-Yau manifolds: a methodological study
99%) accuracy for $h^{1, 1}$ using a neural network inspired by the Inception model for the old dataset, using only 30% (resp.
Inception Neural Network for Complete Intersection Calabi-Yau 3-folds
We introduce a neural network inspired by Google's Inception model to compute the Hodge number $h^{1, 1}$ of complete intersection Calabi-Yau (CICY) 3-folds.
