803 citations · 844 across the 8 of their papers we have counts for
8 papers
Causal Discovery for Irregularly Time Series with Consistency Guarantees
Weihong Li, Baohong Li, Anpeng Wu +4
This paper studies causal discovery in irregularly sampled time series-a key challenge in risk-sensitive domains like finance, healthcare, and climate science, where missing data a…
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
Zexin Wang, Changhua Pei, Minghua Ma +9
Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to…
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection
Haotian Si, Changhua Pei, Zhihan Li +7
Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system. Accurately…
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance
Bixing Yan, Shaoling Chen, Yuxuan He +1
Natural language understanding(NLU) is challenging for finance due to the lack of annotated data and the specialized language in that domain. As a result, researchers have proposed…
Multi-Level Fine-Tuning: Closing Generalization Gaps in Approximation of Solution Maps under a Limited Budget for Training Data
Zhihan Li, Yuwei Fan, Lexing Ying
In scientific machine learning, regression networks have been recently applied to approximate solution maps (e.g., potential-ground state map of Schrödinger equation). In this pape…
Feature Space Singularity for Out-of-Distribution Detection
Haiwen Huang, Zhihan Li, Lulu Wang +3
Out-of-Distribution (OoD) detection is important for building safe artificial intelligence systems. However, current OoD detection methods still cannot meet the performance require…
On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder
Haowen Xu, Wenxiao Chen, Jinlin Lai +3
Using powerful posterior distributions is a popular approach to achieving better variational inference. However, recent works showed that the aggregated posterior may fail to match…
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
Haowen Xu, Wenxiao Chen, Nengwen Zhao +10
To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applicatio…