2 citations · 2 across the 3 of their papers we have counts for
5 papers
GT-PSSM: Unified Probabilistic Framework for Stochastic Dynamics Modeling and Dependency Learning in Multivariate Time Series Anomaly Detection
Wonmo Koo, Jaeyeong Lee, Taeseong Yoon +1
Multivariate time series anomaly detection (MTAD) is crucial for ensuring the safe and reliable operation of complex systems. Many existing methods learn normal patterns by trainin…
Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes
Jaeyeong Lee, Taeseong Yoon, Wonmo Koo +1
Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process…
Courtroom Analogy: New Perspective on Uncertainty-Aware Classification
Taeseong Yoon, Heeyoung Kim
Single-pass uncertainty quantification (UQ) methods for classification represent uncertainty by predicting a tractable distribution over the class probability vector. While existin…
Uncertainty Estimation by Flexible Evidential Deep Learning
Taeseong Yoon, Heeyoung Kim
Uncertainty quantification (UQ) is crucial for deploying machine learning models in high-stakes applications, where overconfident predictions can lead to serious consequences. An e…
Uncertainty Estimation by Density Aware Evidential Deep Learning
Taeseong Yoon, Heeyoung Kim
Evidential deep learning (EDL) has shown remarkable success in uncertainty estimation. However, there is still room for improvement, particularly in out-of-distribution (OOD) detec…