The introduction of Transformer neural networks has changed the landscape of Natural
Language Processing during the last three years. While models inspired by it have …

Machine learning models with high accuracy on test data can still produce systematic
failures, such as harmful biases and safety issues, when deployed in the real world. To …

Interfaces for machine learning (ML), information and visualizations about models or data,
can help practitioners build robust and responsible ML systems. Despite their benefits …

The confusion matrix, a ubiquitous visualization for helping people evaluate machine
learning models, is a tabular layout that compares predicted class labels against actual …

Recent developments in machine learning applications are deeply concerned with the poor
interpretability of most of these techniques. To gain some insights in the process of …

Data scientists require rich mental models of how AI systems behave to effectively train,
debug, and work with them. Despite the prevalence of AI analysis tools, there is no general …

In machine learning, the presumably best model is selected from a variety of model
candidates generated by testing different model types, hyperparameters, or feature subsets …

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 introduction of machine learning to small molecule research–an inherently
multidisciplinary field in which chemists and data scientists combine their expertise and …