Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

Recent studies have indicated that foundation models, such as BERT and GPT, excel at
adapting to various downstream tasks. This adaptability has made them a dominant force in …

Whether AI explanations can help users achieve specific tasks efficiently (ie, usable
explanations) is significantly influenced by their visual presentation. While many techniques …

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 …

Existing model evaluation tools mainly focus on evaluating classification models, leaving a
gap in evaluating more complex models, such as object detection. In this paper, we develop …

The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly
affect the model performance. When the performance is not satisfactory, it is usually difficult …

Time-series data is widely studied in various scenarios, like weather forecast, stock market,
customer behavior analysis. To comprehensively learn about the dynamic environments, it is …

With the advent of the fourth industrial revolution, data-driven decision making has also
become an integral part of decision making. At the same time, deep learning is one of the …

Assigning discriminable and harmonic colors to samples according to their class labels and
spatial distribution can generate attractive visualizations and facilitate data exploration …

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