Search Results for author:
Found 2 papers, 1 papers with code
Optimal Compression of Locally Differentially Private Mechanisms
Compressing the output of \epsilon-locally differentially private (LDP) randomizers naively leads to suboptimal utility.
Accelerating Training of Deep Neural Networks with a Standardization Loss
We find that a standardization loss accelerates training on both small- and large-scale image classification experiments, works with a variety of architectures, and is largely robust to training across different batch sizes.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
