Summary of Entropy Coding Of Unordered Data Structures, by Julius Kunze et al.
Entropy Coding of Unordered Data Structures
by Julius Kunze, Daniel Severo, Giulio Zani, Jan-Willem van de Meent, James Townsend
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Information Theory (cs.IT)
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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 paper introduces shuffle coding, a novel approach for compressing sequences of unordered objects using bits-back coding. The method can be applied to various data structures such as multisets, graphs, and hypergraphs. To facilitate widespread adoption, the authors release an implementation that can be easily adapted to different data types and statistical models. Experimental results show that their implementation achieves state-of-the-art compression rates on a range of graph datasets, including molecular data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding new ways to make data smaller while keeping it just as useful. Imagine you have a big library with many books, but most of the books are very similar. You can save space by grouping the same books together and only storing a special code that says “book 1, book 2, and book 3 are all the same”. This is kind of like what this paper does, but for computer data. It makes it possible to shrink big files down to smaller sizes while still being able to use them. |
