activity
20182022
most citedEvent2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection

6 citations · 12 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG20223 cited

SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction

Xiaoting Li, Yuhang Wu, Vineeth Rakesh +3

Graph neural networks have achieved significant success in representation learning. However, the performance gains come at a cost; acquiring comprehensive labeled data for training…

cs.LG20216 cited

Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection

Yuhang Wu, Mengting Gu, Lan Wang +3

Modeling inter-dependencies between time-series is the key to achieve high performance in anomaly detection for multivariate time-series data. The de-facto solution to model the de…

cs.CV20201 cited

Beating Attackers At Their Own Games: Adversarial Example Detection Using Adversarial Gradient Directions

Yuhang Wu, Sunpreet S. Arora, Yanhong Wu +1

Adversarial examples are input examples that are specifically crafted to deceive machine learning classifiers. State-of-the-art adversarial example detection methods characterize a…

cs.IR2020

GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation

Aravind Sankar, Yanhong Wu, Yuhang Wu +3

We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together. Existing studies target persistent…

cs.CV20202 cited

Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study

Dinh-Luan Nguyen, Sunpreet S. Arora, Yuhang Wu +1

Deep learning-based systems have been shown to be vulnerable to adversarial attacks in both digital and physical domains. While feasible, digital attacks have limited applicability…

cs.CV2019

Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition

Yuhang Wu, Ioannis A. Kakadiaris

Concatenation of the deep network representations extracted from different facial patches helps to improve face recognition performance. However, the concatenated facial template i…