Summary of Density-regression: Efficient and Distance-aware Deep Regressor For Uncertainty Estimation Under Distribution Shifts, by Ha Manh Bui and Anqi Liu
Density-Regression: Efficient and Distance-Aware Deep Regressor for Uncertainty Estimation under Distribution Shifts
by Ha Manh Bui, Anqi Liu
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This abstract proposes a novel method called Density-Regression for uncertainty estimation in neural networks, which achieves fast inference by a single forward pass. Unlike traditional methods that require multiple passes, Density-Regression leverages the density function to estimate uncertainty while maintaining strong performance. The authors demonstrate the effectiveness of this approach on various tasks, including regression, time series forecasting, and depth estimation under distribution shifts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Density-Regression is a new way to do something with machines learning. It’s like a shortcut that makes computers think faster and more accurately about how certain they are. This helps machines make better predictions when the rules change or there’s unexpected information. The idea is tested on some sample problems, and it seems to work as well as other methods but is faster and uses less storage. |
Keywords
* Artificial intelligence * Depth estimation * Inference * Regression * Time series
