16 citations · 56 across the 25 of their papers we have counts for
9 papers · 1 filter
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…
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing
Lu Yin, Vlado Menkovski, Yulong Pei +1
Few-shot classification tasks aim to classify images in query sets based on only a few labeled examples in support sets. Most studies usually assume that each image in a task has a…
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…
The Banking Transactions Dataset and its Comparative Analysis with Scale-free Networks
Akrati Saxena, Yulong Pei, Jan Veldsink +3
We construct a network of 1.6 million nodes from banking transactions of users of Rabobank. We assign two weights on each edge, which are the aggregate transferred amount and the t…
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…
A Survey on Role-Oriented Network Embedding
Pengfei Jiao, Xuan Guo, Ting Pan +2
Recently, Network Embedding (NE) has become one of the most attractive research topics in machine learning and data mining. NE approaches have achieved promising performance in var…
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…
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks
Tianjin Huang, Yulong Pei, Vlado Menkovski +1
Recent years have witnessed an upsurge of interest in the problem of anomaly detection on attributed networks due to its importance in both research and practice. Although various…
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…