A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series

no code implementations • 6 May 2024 • Ziquan Deng, Xiwei Xuan, Kwan-Liu Ma, Zhaodan Kong

Time series anomaly detection is a critical machine learning task for numerous applications, such as finance, healthcare, and industrial systems.

Anomaly Detection Time Series +1

SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective

no code implementations • 1 Mar 2023 • Xiwei Xuan, Ziquan Deng, Hsuan-Tien Lin, Zhaodan Kong, Kwan-Liu Ma

Researchers have proposed various methods for visually interpreting the Convolutional Neural Network (CNN) via saliency maps, which include Class-Activation-Map (CAM) based approaches as a leading family.