Imbalanced data distributions are prevalent in real-world classification and regression tasks.
Data augmentation is a commonly employed technique to mitigate this issue, with implicit …

Despite the widespread use of ionizable lipid nanoparticles (LNPs) in clinical applications
for messenger RNA (mRNA) delivery, the mRNA drug delivery system faces an efficient …

Deep imbalanced regression (DIR), where the target values have a highly skewed
distribution and are also continuous, is an intriguing yet under-explored problem in machine …

Data is essential to performing time series analysis utilizing machine learning approaches,
whether for classic models or today's large language models. A good time-series dataset is …

The field of imbalanced self-supervised learning, especially in the context of tabular data,
has not been extensively studied. Existing research has predominantly focused on image …

Learning from an imbalanced distribution presents a major challenge in predictive modeling,
as it generally leads to a reduction in the performance of standard algorithms. Various …

In supervised learning, it is quite frequent to be confronted with real imbalanced datasets.
This situation leads to a learning difficulty for standard algorithms. Research and solutions in …

Imbalance visual regression is an important and challenging task, and research on it is still
in its early stages. Among the existing solutions, either additional calibration layers need to …