Summary of Robust Calibration For Improved Weather Prediction Under Distributional Shift, by Sankalp Gilda et al.
Robust Calibration For Improved Weather Prediction Under Distributional Shift
by Sankalp Gilda, Neel Bhandari, Wendy Mak, Andrea Panizza
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Improving out-of-domain weather prediction and uncertainty estimation is a crucial challenge in the field of robustness and uncertainty under real-world distributional shifts. A recent paper presents results on tackling this problem by combining a mixture of experts with advanced data augmentation techniques borrowed from computer vision, along with robust post-hoc calibration of predictive uncertainties. The approach yields more accurate and better-calibrated results using deep neural networks compared to boosted tree models for tabular data. The study quantifies its predictions using various metrics and proposes future research directions to further improve performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Weather prediction is tricky! A new study shows that by combining special techniques from computer vision with super smart computers, we can get better weather forecasts. Normally, these models struggle when the weather changes unexpectedly, but this approach helps them stay accurate even when things get weird. The research uses fancy math to measure how good the predictions are and suggests ways to make it even better. |
Keywords
* Artificial intelligence * Data augmentation * Mixture of experts
