78 citations · 112 across the 11 of their papers we have counts for
5 papers · 1 filter
TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
Huiyuan Chen, Xiaoting Li, Kaixiong Zhou +4
There has been an explosion of interest in designing various Knowledge Graph Neural Networks (KGNNs), which achieve state-of-the-art performance and provide great explainability fo…
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…
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…
Towards Generating Adversarial Examples on Mixed-type Data
Han Xu, Menghai Pan, Zhimeng Jiang +4
The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues. For many safety-critical ML tasks, such as…
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,…