52 citations · 289 across the 30 of their papers we have counts for
5 papers · 1 filter
Attentional-Biased Stochastic Gradient Descent
Qi Qi, Yi Xu, Rong Jin +2
In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple mod…
WeMix: How to Better Utilize Data Augmentation
Yi Xu, Asaf Noy, Ming Lin +3
Data augmentation is a widely used training trick in deep learning to improve the network generalization ability. Despite many encouraging results, several recent studies did point…
Towards Understanding Label Smoothing
Yi Xu, Yuanhong Xu, Qi Qian +2
Label smoothing regularization (LSR) has a great success in training deep neural networks by stochastic algorithms such as stochastic gradient descent and its variants. However, th…
An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives
Qi Qi, Zhishuai Guo, Yi Xu +2
In this paper, we propose a practical online method for solving a class of distributionally robust optimization (DRO) with non-convex objectives, which has important applications i…
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization
Yan Yan, Yi Xu, Qihang Lin +2
Epoch gradient descent method (a.k.a. Epoch-GD) proposed by Hazan and Kale (2011) was deemed a breakthrough for stochastic strongly convex minimization, which achieves the optimal…