Generative text‐to‐image models (as exemplified by DALL‐E, MidJourney, and Stable
Diffusion) have recently made enormous technological leaps, demonstrating impressive …

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 …

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 …

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

Over the past years, an increasing number of publications in information visualization,
especially within the field of visual analytics, have mentioned the term “embedding” when …

In the rapidly evolving field of information visualization, rigorous evaluation is essential for
validating new techniques, understanding user interactions, and demonstrating the …

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 …

Deep learning models are widely used in critical applications, highlighting the need for pre-
deployment model understanding and improvement. Visual concept-based methods, while …

Evaluating large language models (LLMs) presents unique challenges. While automatic
side-by-side evaluation, also known as LLM-as-a-judge, has become a promising solution …

Label set construction—deciding on a group of distinct labels—is an essential stage in
building a supervised machine learning (ML) application, as a badly designed label set …