Summary of Probabilities-informed Machine Learning, by Mohsen Rashki
Probabilities-Informed Machine Learning
by Mohsen Rashki
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Probability (math.PR)
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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 machine learning (ML) paradigm integrates probabilistic insights from domain knowledge to enhance model accuracy and mitigate risks of overfitting and underfitting. Building upon physics-informed ML, this approach embeds the probabilistic structure of the target variable into the training process, using historical data or structural reliability methods during experimental design. Applications in regression, image denoising, and classification demonstrate the technique’s effectiveness in addressing real-world problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new machine learning method that helps make predictions more accurate. It does this by combining what we know about how things work with mathematical rules from probability theory. This approach is inspired by physics-informed ML but uses probabilities instead of physical laws. By using these probabilistic insights, the technique can improve model accuracy and avoid common problems like overfitting or underfitting. The method was tested on different types of problems and showed promising results. |
Keywords
» Artificial intelligence » Classification » Image denoising » Machine learning » Overfitting » Probability » Regression » Underfitting
