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Found 4 papers, 1 papers with code
Sparse Variational Contaminated Noise Gaussian Process Regression for Forecasting Geomagnetic Perturbations
Gaussian Processes (GP) have become popular machine learning methods for kernel based learning on datasets with complicated covariance structures.
Limit Theorems for Multi-Group Curie-Weiss Models via the Method of Moments
We study a multi-group version of the mean-field or Curie-Weiss spin model.
Probability Mathematical Physics Mathematical Physics 60F05, 82B20
Local Central Limit Theorem for Multi-Group Curie-Weiss Models
We define a multi-group version of the mean-field spin model, also called Curie-Weiss model.
Probability Mathematical Physics Mathematical Physics 60F05, 82B20
Predicting solar flares with machine learning: investigating solar cycle dependence
A deep learning network, Long-Short Term Memory (LSTM) network, is used in this work to predict whether the maximum flare class an active region (AR) will produce in the next 24 hours is class $\Gamma$.
Solar and Stellar Astrophysics
