Entropy Coding of Unordered Data Structures
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913469920444416 |
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| author | Kunze, Julius Severo, Daniel Zani, Giulio van de Meent, Jan-Willem Townsend, James |
| author_facet | Kunze, Julius Severo, Daniel Zani, Giulio van de Meent, Jan-Willem Townsend, James |
| contents | We present shuffle coding, a general method for optimal compression of sequences of unordered objects using bits-back coding. Data structures that can be compressed using shuffle coding include multisets, graphs, hypergraphs, and others. We release an implementation that can easily be adapted to different data types and statistical models, and demonstrate that our implementation achieves state-of-the-art compression rates on a range of graph datasets including molecular data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08837 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Entropy Coding of Unordered Data Structures Kunze, Julius Severo, Daniel Zani, Giulio van de Meent, Jan-Willem Townsend, James Machine Learning Data Structures and Algorithms Information Theory We present shuffle coding, a general method for optimal compression of sequences of unordered objects using bits-back coding. Data structures that can be compressed using shuffle coding include multisets, graphs, hypergraphs, and others. We release an implementation that can easily be adapted to different data types and statistical models, and demonstrate that our implementation achieves state-of-the-art compression rates on a range of graph datasets including molecular data. |
| title | Entropy Coding of Unordered Data Structures |
| topic | Machine Learning Data Structures and Algorithms Information Theory |
| url | https://arxiv.org/abs/2408.08837 |