Summary of Attention Based Machine Learning Methods For Data Reduction with Guaranteed Error Bounds, by Xiao Li and Jaemoon Lee and Anand Rangarajan and Sanjay Ranka
Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds
by Xiao Li, Jaemoon Lee, Anand Rangarajan, Sanjay Ranka
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Medium Difficulty summary: This paper proposes an attention-based hierarchical compression method for scientific applications in fields like high energy physics, computational fluid dynamics, and climate science. The proposed method leverages strong spatial and temporal correlations in these datasets by introducing an attention-based hyper-block autoencoder to capture inter-block relationships, followed by a block-wise encoder for block-specific information. A PCA-based post-processing step ensures error bounds for each data block. Compared to state-of-the-art SZ3, the proposed method achieves up to 8 times higher compression ratio on the multi-variable S3D dataset and up to 3 times and 2 times higher compression ratios on single-variable E3SM and XGC datasets, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper helps solve a big problem in science where we collect huge amounts of data, but it’s too much for computers and storage devices to handle. The scientists propose a new way to compress this data by grouping similar information together and finding patterns that repeat throughout the data. Their method works really well on different types of data sets and can even reduce the amount of data by up to 8 times! This is important because it will help us better understand complex scientific problems, like climate change and weather forecasting. |
Keywords
» Artificial intelligence » Attention » Autoencoder » Encoder » Pca
