16 citations · 56 across the 25 of their papers we have counts for
14 papers · 1 filter
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation
Ricky Maulana Fajri, Yulong Pei, Lu Yin +1
Despite significant advancements in active learning and adversarial attacks, the intersection of these two fields remains underexplored, particularly in developing robust active le…
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…
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Tianjin Huang, Tianlong Chen, Meng Fang +8
Recent works have impressively demonstrated that there exists a subnetwork in randomly initialized convolutional neural networks (CNNs) that can match the performance of the fully…
FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering
Ricky Fajri, Akrati Saxena, Yulong Pei +1
Active Learning (AL) techniques have proven to be highly effective in reducing data labeling costs across a range of machine learning tasks. Nevertheless, one known challenge of th…
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
Lu Yin, Vlado Menkovski, Meng Fang +5
Recent works on sparse neural network training (sparse training) have shown that a compelling trade-off between performance and efficiency can be achieved by training intrinsically…
A Comparative Study on Robust Graph Neural Networks to Structural Noises
Zeyu Zhang, Yulong Pei
Graph neural networks (GNNs) learn node representations by passing and aggregating messages between neighboring nodes. GNNs have been applied successfully in several application do…
Calibrated Adversarial Training
Tianjin Huang, Vlado Menkovski, Yulong Pei +1
Adversarial training is an approach of increasing the robustness of models to adversarial attacks by including adversarial examples in the training set. One major challenge of prod…
On Generalization of Graph Autoencoders with Adversarial Training
Tianjin Huang, Yulong Pei, Vlado Menkovski +1
Adversarial training is an approach for increasing model's resilience against adversarial perturbations. Such approaches have been demonstrated to result in models with feature rep…
Direction-Aggregated Attack for Transferable Adversarial Examples
Tianjin Huang, Vlado Menkovski, Yulong Pei +2
Deep neural networks are vulnerable to adversarial examples that are crafted by imposing imperceptible changes to the inputs. However, these adversarial examples are most successfu…
Selfish Sparse RNN Training
Shiwei Liu, Decebal Constantin Mocanu, Yulong Pei +1
Sparse neural networks have been widely applied to reduce the computational demands of training and deploying over-parameterized deep neural networks. For inference acceleration, m…
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks
Yulong Pei, Tianjin Huang, Werner van Ipenburg +1
Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection. Existing approaches h…