Dong Yin

Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation

2019 · arXiv (Cornell University)

Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions. While architectural advances have led to improved accuracy, building robust models remains challenging. Prior …

Architecture Matters in Continual Learning

2022 · arXiv (Cornell University)

A large body of research in continual learning is devoted to overcoming the catastrophic forgetting of neural networks by designing new algorithms that are robust to the distribution shifts. However, the majority of these works …