Publications (6)
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…
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection
Lan Wang, Yusan Lin, Yuhang Wu +3
Today's cyber-world is vastly multivariate. Metrics collected at extreme varieties demand multivariate algorithms to properly detect anomalies. However, forecast-based algorithms,…
Denoising Self-attentive Sequential Recommendation
Huiyuan Chen, Yusan Lin, Menghai Pan +6
Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies. This is mainly attributed to their unique self…
Fashion Recommendation and Compatibility Prediction Using Relational Network
Maryam Moosaei, Yusan Lin, Hao Yang
Fashion is an inherently visual concept and computer vision and artificial intelligence (AI) are playing an increasingly important role in shaping the future of this domain. Many r…
Predicting Next-Season Designs on High Fashion Runway
Yusan Lin, Hao Yang
Fashion is a large and fast-changing industry. Foreseeing the upcoming fashion trends is beneficial for fashion designers, consumers, and retailers. However, fashion trends are oft…
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…