7 citations · 11 across the 7 of their papers we have counts for
10 papers
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…
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…