A Note on Randomized Element-wise Matrix Sparsification

Abstract

Given a matrix A \in R^{m x n}, we present a randomized algorithm that sparsifies A by retaining some of its elements by sampling them according to a distribution that depends on both the square and the absolute value of the entries. We combine the ideas of [4, 1] and provide an elementary proof of the approximation accuracy of our algorithm following [4] without the truncation step.

5 pages

References in corpus (1)

• A Simpler Approach to Matrix Completion

Cited by in corpus (6)

• Provable Filter Pruning for Efficient Neural Networks
• Data-Independent Neural Pruning via Coresets
• Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
• Data-Independent Structured Pruning of Neural Networks via Coresets
• Global Capacity Measures for Deep ReLU Networks via Path Sampling
• Near-Optimal Entrywise Sampling of Numerically Sparse Matrices