Learning Sketch Matrices with Optimized Positions and Values for Efficient Data Processing

This work proposes the first learning-based algorithms that optimize both the locations and values of the non-zero entries in sketching matrices, leading to significant improvements in accuracy and efficiency over classical sketching techniques and previous learning-based approaches.