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 …

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 …

Constructing supervised machine learning models for real-world video analysis require
substantial labeled data, which is costly to acquire due to scarce domain expertise and …

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

Label quality issues, such as noisy labels and imbalanced class distributions, have negative
effects on model performance. Automatic reweighting methods identify problematic samples …

Visualization for machine learning (VIS4ML) research aims to help experts apply their prior
knowledge to develop, understand, and improve the performance of machine learning …

Temporal action localization aims to identify the boundaries and categories of actions in
videos, such as scoring a goal in a football match. Single-frame supervision has emerged as …

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN)
have experienced great advancement in the past few years, harnessing the advantages of …

Recent advances in artificial intelligence largely benefit from better neural network
architectures. These architectures are a product of a costly process of trial-and-error. To …