Optimizing Learned Bloom Filters by Sandwiching
We provide a simple method for improving the performance of the recently introduced learned Bloom filters, by showing that they perform better when the learned function is sandwiched between two Bloom filters.
We study a new technique for optimal data compression subject to conditions of causality and different types of memory. The technique is based on the assumption that some information about compressed data can be obtained from a solution of the associated problem without constraints of causality and memory. This allows us to consider two separate problem related to compression and de- compression subject to those constraints. Their solutions are given and the analysis of the associated errors is provided. Keywords—stochastic signals, optimization problems in signal processing.