The use and creation of machine‐learning‐based solutions to solve problems or reduce
their computational costs are becoming increasingly widespread in many domains. Deep …

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 …

Traditional deep learning interpretability methods which are suitable for model users cannot
explain network behaviors at the global level and are inflexible at providing fine-grained …

Data-centric AI has emerged as a new research area to systematically engineer the data to
land AI models for real-world applications. As a core method for data-centric AI, data …

The last decade has witnessed many visual analytics (VA) systems that make successful
applications to wide-ranging domains like urban analytics and explainable AI. However …

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 …

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

CNN image classifiers are widely used, thanks to their efficiency and accuracy. However,
they can suffer from biases that impede their practical applications. Most existing bias …

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 …

Abstract We present ScrutinAI, a Visual Analytics tool to leverage semantic understanding
for deep neural network (DNN) prediction analysis, focusing on models for object detection …