16 citations · 56 across the 25 of their papers we have counts for
5 papers · 1 filter
A Structural-Clustering Based Active Learning for Graph Neural Networks
Ricky Maulana Fajri, Yulong Pei, Lu Yin +1
In active learning for graph-structured data, Graph Neural Networks (GNNs) have shown effectiveness. However, a common challenge in these applications is the underutilization of cr…
Heterophily-Based Graph Neural Network for Imbalanced Classification
Zirui Liang, Yuntao Li, Tianjin Huang +3
Graph neural networks (GNNs) have shown promise in addressing graph-related problems, including node classification. However, conventional GNNs assume an even distribution of data…
MOLE: MOdular Learning FramEwork via Mutual Information Maximization
Tianchao Li, Yulong Pei
This paper is to introduce an asynchronous and local learning framework for neural networks, named Modular Learning Framework (MOLE). This framework modularizes neural networks by…
Enhancing Adversarial Training via Reweighting Optimization Trajectory
Tianjin Huang, Shiwei Liu, Tianlong Chen +6
Despite the fact that adversarial training has become the de facto method for improving the robustness of deep neural networks, it is well-known that vanilla adversarial training s…
Deep Functional Factor Models: Forecasting High-Dimensional Functional Time Series via Bayesian Nonparametric Factorization
Yirui Liu, Xinghao Qiao, Yulong Pei +1
This paper introduces the Deep Functional Factor Model (DF2M), a Bayesian nonparametric model designed for analysis of high-dimensional functional time series. DF2M is built upon t…