Summary of Interpretable Multi-source Data Fusion Through Latent Variable Gaussian Process, by Sandipp Krishnan Ravi et al.
Interpretable Multi-Source Data Fusion Through Latent Variable Gaussian Process
by Sandipp Krishnan Ravi, Yigitcan Comlek, Arjun Pathak, Vipul Gupta, Rajnikant Umretiya, Andrew Hoffman, Ghanshyam Pilania, Piyush Pandita, Sayan Ghosh, Nathaniel Mckeever, Wei Chen, Liping Wang
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Latent Variable Gaussian Process (LVGP) framework enables multi-source data fusion by mapping individual data sources into a physically interpretable latent space. This approach addresses the issue of differences in quality and comprehensiveness of underlying physical parameters across various information sources. The LVGP method tags each source as a characteristic categorical variable, allowing for source-aware data fusion modeling. A dissimilarity metric is also introduced to study these differences. Case studies in mathematics and materials science demonstrate that this multi-source framework outperforms single-source and source-unaware machine learning models in sparse-data problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have been using artificial intelligence and machine learning to understand complex systems by combining data from different sources. However, they haven’t considered the quality of these sources before. This can affect how well their models work. Researchers propose a new way to combine data from multiple sources called Latent Variable Gaussian Process (LVGP). LVGP helps us understand which source is more reliable and creates a better model for sparse-data problems. |
Keywords
* Artificial intelligence * Latent space * Machine learning
