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feature encoding

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papersTODAY 04:00 UTC

Entropy-Punctured Bloom Filters Target Memory-Efficient ML Feature Encoding

A new arXiv paper proposes entropy-punctured Bloom filters as a compact way to represent features when machine learning pipelines face limits on storage, bandwidth, transmission cost, or data privacy. Bloom filter encodings are probabilistic and space-efficient, and the work examines how puncturing based on entropy affects their memory footprint and usability. The approach is aimed at settings where raw data cannot be stored or shared freely.

research/bloom-filters/memory-efficiency/privacy