Summary of Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting, by Daniel Iong et al.
Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting
by Daniel Iong, Matthew McAnear, Yuezhou Qu, Shasha Zou, Gabor Toth, Yang Chen
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed extension to Gaussian Processes (GP) addresses issues with heteroscedastic variance and outlier noise by introducing a contaminated normal likelihood function. This novel framework is combined with the Sparse Variational Gaussian Process (SVGP) method for scalable inference on large datasets. The approach is demonstrated through an application to geomagnetic ground perturbations, where it outperforms state-of-the-art neural network-based models in terms of prediction interval accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gaussian Processes are a type of machine learning that can handle complex data relationships. In this paper, scientists improve GPs by adding a new way to deal with noisy or unusual data points. They also create a faster method for using these improved GPs on big datasets. The researchers test their approach on predicting changes in the Earth’s magnetic field and find it works better than usual neural network-based methods. |
Keywords
* Artificial intelligence * Inference * Likelihood * Machine learning * Neural network
