Summary of Distribution Free Uncertainty Quantification in Neuroscience-inspired Deep Operators, by Shailesh Garg and Souvik Chakraborty
Distribution free uncertainty quantification in neuroscience-inspired deep operators
by Shailesh Garg, Souvik Chakraborty
First submitted to arxiv on: 12 Dec 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 paper introduces a framework called Conformalized Randomized Prior Operator (CRP-O) that quantifies uncertainty in both conventional and spiking neural networks. This framework combines Randomized Prior networks, Split Conformal Prediction, and Gaussian Process Regression to predict uncertainty bounds. The authors demonstrate the effectiveness of their approach by integrating it with a Variable Spiking Wavelet Neural Operator (VSWNO) and applying it to four partial differential equation examples. Results show that the CRP-O framework improves uncertainty estimates compared to other methods like Quantile WNO, Conformalized Quantile WNO, and vanilla RP-VSWNO. The proposed approach has potential for practical applications in energy-efficient deep learning algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers learn better while using less energy. It introduces a new way to measure how sure or unsure the computer is about its answers. This helps people know when the computer’s answer might not be correct. The method uses ideas from science and math to combine different types of learning, like old-fashioned computers and newer brain-inspired computers. The authors test their approach on some complex math problems and show that it does a better job than other methods at guessing how sure or unsure the computer is. |
Keywords
» Artificial intelligence » Deep learning » Regression
