activity
20202022
most citedA Trainable Reconciliation Method for Hierarchical Time-Series

11 citations · 15 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2022

A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram

Hao-Chun Yang, Wan-Ting Hsieh, Trista Pei-Chun Chen

Electrocardiogram(ECG) is commonly used to detect cardiac irregularities such as atrial fibrillation, bradycardia, and other irregular complexes. While previous studies have achiev…

cs.CV2022

Domain-Generalized Textured Surface Anomaly Detection

Shang-Fu Chen, Yu-Min Liu, Chia-Ching Lin +2

Anomaly detection aims to identify abnormal data that deviates from the normal ones, while typically requiring a sufficient amount of normal data to train the model for performing…

cs.LG202111 cited

A Trainable Reconciliation Method for Hierarchical Time-Series

Davide Burba, Trista Chen

In numerous applications, it is required to produce forecasts for multiple time-series at different hierarchy levels. An obvious example is given by the supply chain in which deman…

cs.CV20204 cited

TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions

Daniel Stanley Tan, Yi-Chun Chen, Trista Pei-Chun Chen +1

In this paper, we propose a framework called TrustMAE to address the problem of product defect classification. Instead of relying on defective images that are difficult to collect…

cs.LG2020

CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion

Ying-Sheng Luo, Jonathan Hans Soeseno, Trista Pei-Chun Chen +1

Motion synthesis in a dynamic environment has been a long-standing problem for character animation. Methods using motion capture data tend to scale poorly in complex environments b…