Summary of Efficient Compression Of Sparse Accelerator Data Using Implicit Neural Representations and Importance Sampling, by Xihaier Luo et al.
Efficient Compression of Sparse Accelerator Data Using Implicit Neural Representations and Importance Sampling
by Xihaier Luo, Samuel Lurvey, Yi Huang, Yihui Ren, Jin Huang, Byung-Jun Yoon
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The proposed approach uses implicit neural representations for data learning and compression, addressing the challenges of high-energy particle collider data. It leverages the sparse nature of tracking detector data to accelerate network training via an importance sampling technique. The method competes with traditional algorithms like MGARD, SZ, and ZFP in terms of accuracy while offering significant speed-ups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have a big problem: they’re collecting huge amounts of data from particle colliders at incredible rates! They need ways to shrink this data down so it’s manageable for storage. One key feature of the data is that most of it is empty space – just tiny particles moving around. This makes traditional compression methods not very effective. The new approach uses special computer programs called neural networks to learn and compress the data in a more efficient way, which can help scientists do their work faster without losing any accuracy. |
Keywords
» Artificial intelligence » Tracking
