Aug 5, 2022 · We introduce a lossless input compression technique that improves the memory consumption of the learned model while preserving a comparable model accuracy.

Dec 6, 2021 · Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes ...

What makes a learned Bloom filter interesting is that it uses a function that can be obtained by “learning” the set K to help determine the appropriate answer;.

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Bloom filters are a form of existence index which offer compressed representa- tions of sets at the cost of some false positives. Recent work has introduced ...

Mar 27, 2024 · A novel methodology, supported by software, for designing, analyzing and implementing learned Bloom filters that account for their own multi-criteria nature.

By using compressed Bloom filters, proxies can reduce the number of bits broadcast, the false positive probability, and/or the amount of computation per lookup.

Bloom filters are probabilistic data structures commonly used for approximate membership problems in many areas of Computer Science (networking, ...

Bloom Filters are space-efficient data structures for membership queries over sets. To enable queries for multiplicities of multi-sets, the bitmap in a Bloom ...

Sep 2, 2023 · We propose the Multi-key Learned Bloom Filter (MLBF), a hybrid method that combines machine learning techniques with the Bloom filter.