activity
20192025
most citedTrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications

8 citations · 25 across the 16 of their papers we have counts for

collaborators

16 papers

cs.LG2025

Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalanced Regression

Yung-Chien Wang, Kuang-Da Wang, Wei-Yao Wang +1

Tabular data serve as a fundamental and ubiquitous representation of structured information in numerous real-world applications, e.g., finance and urban planning. In the realm of t…

cs.LG2024

Text2Freq: Learning Series Patterns from Text via Frequency Domain

Ming-Chih Lo, Ching Chang, Wen-Chih Peng

Traditional time series forecasting models mainly rely on historical numeric values to predict future outcomes.While these models have shown promising results, they often overlook…

cs.LG2024

COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data

Ting-Yun Ou, Ching Chang, Wen-Chih Peng

Understanding causal relationships between machines is crucial for fault diagnosis and optimization in manufacturing processes. Real-world datasets frequently exhibit up to 90% mis…

cs.CL20241 cited

MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models

Thao Minh Nguyen Phan, Cong-Tinh Dao, Chenwei Wu +7

Electronic health records (EHRs) are multimodal by nature, consisting of structured tabular features like lab tests and unstructured clinical notes. In real-life clinical practice,…

cs.LG20242 cited

Root Cause Analysis In Microservice Using Neural Granger Causal Discovery

Cheng-Ming Lin, Ching Chang, Wei-Yao Wang +2

In recent years, microservices have gained widespread adoption in IT operations due to their scalability, maintenance, and flexibility. However, it becomes challenging for site rel…

cs.CL20231 cited

RSVP: Customer Intent Detection via Agent Response Contrastive and Generative Pre-Training

Yu-Chien Tang, Wei-Yao Wang, An-Zi Yen +1

The dialogue systems in customer services have been developed with neural models to provide users with precise answers and round-the-clock support in task-oriented conversations by…