Before revision: Learned Bloom filters enhance standard Bloom filters by using a learned model for the represented data set.
After revision: Bloom filters are space-efficient probabilistic data structures that are used to test whether an element is a member of a set, and may return false positives. Recently, variations referred to as learned Bloom filters were developed that can provide improved performance in terms of the rate of false positives, by using a learned model for the represented data set.