20 citations · 23 across the 4 of their papers we have counts for
4 papers
Bringing Clustering to MLL: Weakly-Supervised Clustering for Partial Multi-Label Learning
Yu Chen, Weijun Lv, Yue Huang +2
Label noise in multi-label learning (MLL) poses significant challenges for model training, particularly in partial multi-label learning (PML) where candidate labels contain both re…
Feature-Label Modal Alignment for Robust Partial Multi-Label Learning
Yu Chen, Weijun Lv, Yue Huang +4
In partial multi-label learning (PML), each instance is associated with a set of candidate labels containing both ground-truth and noisy labels. The presence of noisy labels disrup…
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization
Chaoqi Chen, Luyao Tang, Feng Liu +3
Domain generalization (DG) enables generalizing a learning machine from multiple seen source domains to an unseen target one. The general objective of DG methods is to learn semant…
GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning
Jiajun Fan, Changnan Xiao, Yue Huang
Deep Q Network (DQN) firstly kicked the door of deep reinforcement learning (DRL) via combining deep learning (DL) with reinforcement learning (RL), which has noticed that the dist…