Real-world classification problems typically exhibit an imbalanced or long-tailed label
distribution, wherein many labels are associated with only a few samples. This poses a …

In the real world, large-scale datasets for visual recognition typically exhibit a long-tailed
distribution, where only a few classes contain adequate samples but the others have (much) …

The objective of fraud detection is to distinguish fraudsters from normal users. In
graph/network environments, both fraudsters and normal users are modeled as nodes, and …

Test-time adaptation (TTA) adapts a source model to the distribution shift in testing data
without using any source data. There have been plenty of algorithms concentrated on …

We developed a fully automated system using a convolutional neural network (CNN) for total
retina segmentation in optical coherence tomography (OCT) that is robust to the presence of …

Despite the impressive performance of recent unbiased Scene Graph Generation (SGG)
methods, the current debiasing literature mainly focuses on the long-tailed distribution …

One crucial challenge of real-world multilingual speech recognition is the long-tailed
distribution problem, where some resource-rich languages like English have abundant …

Deep learning-based vulnerability prediction approaches are proposed to help under-
resourced security practitioners to detect vulnerable functions. However, security …

We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes
enhanced by machine learning techniques as a path towards ultra-reliable and low-latency …

Empirical risk minimization (ERM) is sensitive to spurious correlations present in training
data, which poses a significant risk when deploying systems trained under this paradigm in …