Summary of Regularized Multi-output Gaussian Convolution Process with Domain Adaptation, by Wang Xinming et al.
Regularized Multi-output Gaussian Convolution Process with Domain Adaptation
by Wang Xinming, Wang Chao, Song Xuan, Kirby Levi, Wu Jianguo
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP)
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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 A novel multi-output Gaussian process (MGP) modeling framework is proposed to overcome two critical challenges in transfer learning: negative transfer and input domain inconsistency. By incorporating sparse covariance matrices and convolutional processes, the framework adaptively selects informative outputs for knowledge transfer, effectively addressing negative transfer. To deal with domain inconsistencies, a marginalization-expansion approach aligns input domains across different outputs. Theoretical guarantees are provided for practical and asymptotic performance. Experimental results on comprehensive simulations and a real-world ceramic manufacturing process demonstrate superior performance over state-of-the-art benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to use Gaussian processes to learn from multiple related tasks. This approach helps to avoid the negative effect of learning something that’s not useful for any of the tasks. They also addressed a problem called domain inconsistency, where the input data looks different across tasks. The team used a combination of techniques to select the most important information and adapt to different domains. The results show that this method outperforms existing approaches in simulations and in a real-world application. |
Keywords
» Artificial intelligence » Transfer learning
