90 citations · 220 across the 23 of their papers we have counts for
6 papers · 2 filters
A Multi-Task Gradient Descent Method for Multi-Label Learning
Lu Bai, Yew-Soon Ong, Tiantian He +1
Multi-label learning studies the problem where an instance is associated with a set of labels. By treating single-label learning problem as one task, the multi-label learning probl…
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling
Yue Wang, Chenwei Zhang, Shen Wang +4
We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future link…
Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking
Yue Wang, Yao Wan, Chenwei Zhang +3
Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps pe…
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification
Lu Bail, Lixin Cui, Yuhang Jiao +2
In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification. Our idea is to tran…
Learning Vertex Convolutional Networks for Graph Classification
Lu Bai, Lixin Cui, Shu Wu +2
In this paper, we develop a new aligned vertex convolutional network model to learn multi-scale local-level vertex features for graph classification. Our idea is to transform the g…
Fused Lasso for Feature Selection using Structural Information
Lu Bai, Lixin Cui, Yue Wang +2
Feature selection has been proven a powerful preprocessing step for high-dimensional data analysis. However, most state-of-the-art methods tend to overlook the structural correlati…