4 citations · 4 across the 1 of their papers we have counts for
3 papers
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness
James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti +2
Successful adoption of deep learning (DL) in the wild requires models to be: (1) compact, (2) accurate, and (3) robust to distributional shifts. Unfortunately, efforts towards simu…
The Generalization-Stability Tradeoff In Neural Network Pruning
Brian R. Bartoldson, Ari S. Morcos, Adrian Barbu +1
Pruning neural network parameters is often viewed as a means to compress models, but pruning has also been motivated by the desire to prevent overfitting. This motivation is partic…
Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks
Brian Bartoldson, Adrian Barbu, Gordon Erlebacher
Artificial neural networks (ANNs) may not be worth their computational/memory costs when used in mobile phones or embedded devices. Parameter-pruning algorithms combat these costs,…