Summary of Machine Learning Techniques For Data Reduction Of Climate Applications, by Xiao Li and Qian Gong and Jaemoon Lee and Scott Klasky and Anand Rangarajan and Sanjay Ranka
Machine Learning Techniques for Data Reduction of Climate Applications
by Xiao Li, Qian Gong, Jaemoon Lee, Scott Klasky, Anand Rangarajan, Sanjay Ranka
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph)
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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 pipelined compression approach uses neural-network-based techniques to identify regions where binary QoI are highly likely to be present, followed by a Guaranteed Autoencoder (GAE) that compresses data with differential error bounds. The GAE leverages QoI information to apply low-error compression only to these regions, resulting in high compression ratios while maintaining downstream goals. Experimental results for climate data generated from the E3SM Simulation model demonstrate the approach’s superiority over comparable methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are trying to figure out a way to compress big datasets without losing important details. They do this by using special computer algorithms that can predict where important information is located in the data. Then, they use another algorithm to actually shrink the data, but only in those areas where it’s most important. This helps them save space and time while still getting accurate results. The researchers tested their method on climate data from a famous model called E3SM and found that it worked better than other similar methods. |
Keywords
» Artificial intelligence » Autoencoder » Neural network
