103 citations · 152 across the 9 of their papers we have counts for
6 papers · 1 filter
Learning Invariant Rules from Data for Interpretable Anomaly Detection
Cheng Feng, Pingge Hu
In the research area of anomaly detection, novel and promising methods are frequently developed. However, most existing studies exclusively focus on the detection task only and ign…
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data
Wenkai Li, Cheng Feng, Ting Chen +1
Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. In recent years, a large number of deep neural network-based method…
A cGAN Ensemble-based Uncertainty-aware Surrogate Model for Offline Model-based Optimization in Industrial Control Problems
Cheng Feng
This study focuses on two important problems related to applying offline model-based optimization to real-world industrial control problems. The first problem is how to create a re…
Nonlinear Hawkes Processes in Time-Varying System
Feng Zhou, Quyu Kong, Yixuan Zhang +2
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena. Although the classic Hawkes processes cover a wide range of…
Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filtering
Cheng Feng, Pengwei Tian
Recent advances in AIoT technologies have led to an increasing popularity of utilizing machine learning algorithms to detect operational failures for cyber-physical systems (CPS).…
StackVAE-G: An efficient and interpretable model for time series anomaly detection
Wenkai Li, Wenbo Hu, Ting Chen +2
Recent studies have shown that autoencoder-based models can achieve superior performance on anomaly detection tasks due to their excellent ability to fit complex data in an unsuper…