The past decade has witnessed a plethora of works that leverage the power of visualization
(VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML …

Traditional deep learning interpretability methods which are suitable for model users cannot
explain network behaviors at the global level and are inflexible at providing fine-grained …

The recent prevalence of publicly accessible, large medical imaging datasets has led to a
proliferation of artificial intelligence (AI) models for cardiovascular image classification and …

Classification models learn to generalize the associations between data samples and their
target classes. However, researchers have increasingly observed that machine learning …

Disease risk models can identify high-risk patients and help clinicians provide more
personalized care. However, risk models de-veloped on one dataset may not generalize …

Pre-trained transformer-based language models are becoming increasingly popular due to
their exceptional performance on various benchmarks. However, concerns persist regarding …

Generative image models have emerged as a promising technology to produce realistic
images. Despite potential benefits, concerns grow about its misuse, particularly in …

In the image classification task deep neural networks frequently rely on bias attributes that
are spuriously correlated with a target class in the presence of dataset bias resulting in …

In the applications of machine learning, it is difficult to ensure the quality of training data due
to the various sources of training data and the inexperience of some annotators. By tightly …

As machine learning (ML) gains wider adoption in real-world applications, the validation of
ML models becomes fundamental for its productization, particularly in safety-critical …