Search Results for author:
Found 2 papers, 0 papers with code
Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias
In this study, we first reveal that a specific finite-difference computation, composed of both gradient ascent and descent steps, reduces the computational cost of GR.
Self-paced Data Augmentation for Training Neural Networks
A typical method that applies data augmentation to all training samples disregards sample suitability, which may reduce classifier performance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
