2 citations · 2 across the 4 of their papers we have counts for
4 papers
Enhance and Reuse: A Dual-Mechanism Approach to Boost Deep Forest for Label Distribution Learning
Jia-Le Xu, Shen-Huan Lyu, Yu-Nian Wang +4
Label distribution learning (LDL) requires the learner to predict the degree of correlation between each sample and each label. To achieve this, a crucial task during learning is t…
Compressing Model with Few Class-Imbalance Samples: An Out-of-Distribution Expedition
Tian-Shuang Wu, Shen-Huan Lyu, Ning Chen +2
In recent years, as a compromise between privacy and performance, few-sample model compression has been widely adopted to deal with limited data resulting from privacy and security…
Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation
Shen-Huan Lyu, Yi-Xiao He, Yanyan Wang +3
Oblique Decision Tree (ODT) separates the feature space by linear projections, as opposed to the conventional Decision Tree (DT) that forces axis-parallel splits. ODT has been prov…
Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learning
Wenxuan Zhou, Zhihao Qu, Shen-Huan Lyu +2
This paper introduces a novel framework designed to achieve a high compression ratio in Split Learning (SL) scenarios where resource-constrained devices are involved in large-scale…