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A framework for learned sparse sketches
In this work, we consider the problem of optimizing sketches to obtain low approximation error over a data distribution.
Learning the Positions in CountSketch
Despite the growing body of work on this paradigm, a noticeable omission is that the locations of the non-zero entries of previous algorithms were fixed, and only their values were learned.
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