Large language models (LLMs) have gained popularity in various fields for their exceptional
capability of generating human-like text. Their potential misuse has raised social concerns …

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

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 …

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 …

The advances in multi-modal foundation models (FMs)(eg, CLIP and LLaVA) have facilitated
the auto-labeling of large-scale datasets, enhancing model performance in challenging …

In natural language processing (NLP), text classification tasks are increasingly fine‐grained,
as datasets are fragmented into a larger number of classes that are more difficult to …

In this paper, we present GameDepot, a visual analytics system designed to enable
interactive analysis of performance testing logs for mobile games. Due to the emergence of …

There is a vast amount of unstructured text data generated every day analyzing and making
sense of these text-based datasets is a complex, cumbersome task. The existing …

在机器学习应用中, 由于数据来源渠道多以及部分标注者水平不足, 训练数据质量很难得到保证.
通过深度结合机器学习和可视化技术, 可视分析技术将人融入数据质量分析与提升回路中 …

Textbooks continue to be one of primary mediums of learning. Students often need
additional support during the process of reading textbooks leading to several research …