The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …

Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …

There are currently no effective biomarkers for diagnosing Parkinson's disease (PD) or
tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD …

Real-world data often exhibits long tail distributions with heavy class imbalance, where the
majority classes can dominate the training process and alter the decision boundaries of the …

Data imbalance exists ubiquitously in real-world visual regressions, eg, age estimation and
pose estimation, hurting the model's generalizability and fairness. Thus, imbalanced …

Self-supervised learning (SSL) is a scalable way to learn general visual representations
since it learns without labels. However, large-scale unlabeled datasets in the wild often have …

Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem
services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral …

Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes
for various biotechnological and industrial applications, but the limited performance of …

Machine learning models often perform poorly on subgroups that are underrepresented in
the training data. Yet, little is understood on the variation in mechanisms that cause …

In many real world settings, imbalanced data impedes model performance of learning
algorithms, like neural networks, mostly for rare cases. This is especially problematic for …