6 papers
Vessel Traffic Flow Prediction on Sparse Data via Spatio-Temporal Graph Neural Networks with a Learnable Tweedie Head
Kyeongjun Lee, Heeyoung Kim
Accurate vessel traffic flow prediction is crucial for smart port operations and navigational safety. However, maritime traffic flow data are often highly sparse with intermittent…
DAMEL: Dual-Axis Multi-Expert Learning for Class-Imbalanced Learning
Hyuck Lee, Taemin Park, Heeyoung Kim
Various algorithms have been proposed to address the challenges posed by class-imbalanced learning from real-world data with long-tailed distributions. While these algorithms reduc…
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…
Simultaneous Long-tailed Recognition and Multi-modal Fusion for Highly Imbalanced Multi-modal Data
Heegeon Yoon, Heeyoung Kim
Long-tailed distributions in class-imbalanced data present a fundamental challenge for deep learning models, which tend to be biased toward majority classes. While recent methods f…
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…