9 citations · 9 across the 2 of their papers we have counts for
9 papers
New Interpretations of Normalization Methods in Deep Learning
Jiacheng Sun, Xiangyong Cao, Hanwen Liang +3
In recent years, a variety of normalization methods have been proposed to help train neural networks, such as batch normalization (BN), layer normalization (LN), weight normalizati…
Locally Differentially Private (Contextual) Bandits Learning
Kai Zheng, Tianle Cai, Weiran Huang +2
We study locally differentially private (LDP) bandits learning in this paper. First, we propose simple black-box reduction frameworks that can solve a large family of context-free…
Boosting Few-Shot Learning With Adaptive Margin Loss
Aoxue Li, Weiran Huang, Xu Lan +3
Few-shot learning (FSL) has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in learning to generalize from a few examples. T…
DARTS+: Improved Differentiable Architecture Search with Early Stopping
Hanwen Liang, Shifeng Zhang, Jiacheng Sun +4
Recently, there has been a growing interest in automating the process of neural architecture design, and the Differentiable Architecture Search (DARTS) method makes the process ava…
Few-Shot Learning with Global Class Representations
Tiange Luo, Aoxue Li, Tao Xiang +2
In this paper, we propose to tackle the challenging few-shot learning (FSL) problem by learning global class representations using both base and novel class training samples. In ea…
Community Exploration: From Offline Optimization to Online Learning
Xiaowei Chen, Weiran Huang, Wei Chen +1
We introduce the community exploration problem that has many real-world applications such as online advertising. In the problem, an explorer allocates limited budget to explore com…