Visualization for Artificial Intelligence

AvShixia Liu,Weikai Yang

Inbunden, Engelska, 2024

Del i serien Synthesis Lectures on Visualization

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Beskrivning

This book explores how visualization provides an effective way of improving not only the interpretability but also the generalization capabilities of machine learning models. In the model pipeline, they support (1) model development by focusing on model understanding, diagnosis, and steering;

Produktinformation

Utgivningsdatum:2024-12-22
Mått:168 x 240 x 14 mm
Vikt:479 g
Format:Inbunden
Språk:Engelska
Serie:Synthesis Lectures on Visualization
Antal sidor:144
Förlag:Springer International Publishing AG
ISBN:9783031753398

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Människa – datorinteraktion inom Data och IT
Databaser inom Data och IT

Mer om författaren

Shixia Liu is a professor at Tsinghua University. Her research interests include explainable machine learning, visual text analytics, and text mining. Shixia was elevated to an IEEE Fellow in 2021 and inducted into IEEE Visualization Academy in 2020. She is an associate editor-in-chief of IEEE Transactions on Visualization and Computer Graphics and is an associate editor of Artificial Intelligence, IEEE Transactions on Big Data, and ACM Transactions on Intelligent Systems and Technology. She was one of the Papers Co-Chairs of IEEE VIS (VAST) 2016 and 2017 and is in the steering committee of IEEE VIS (2020–2023).Weikai Yang is an Assistant Professor at the Data Science and Analytics Trust, holding a joint appointment at the Computational Media and Arts Thrust (CMA) in the Information Hub, at The Hong Kong University of Science and Technology (Guangzhou). He received his Ph.D. in Software Engineering under the supervision of professor Shixia Liu and his B.S. degrees from Tsinghua University. His research primarily focuses on the intersections between visual analysis and machine learning, with the goal of helping general users to understand large-scale data and utilize machine learning models more effectively and efficiently by incorporating their knowledge and feedback.Junpeng Wang is a Research Scientist at Visa Research. He received his B.Eng. degree in software engineering from Nankai University in 2011, his M.S. degree in computer science from Virginia Tech in 2015, and his Ph.D. degree in computer science from the Ohio State University in 2019. Junpeng's research interests lie broadly in explainable artificial intelligence, visual analytics, and deep learning. He is the recipient of the 2021 IEEE TVCG Best Reviewer Award and multiple best paper awards, including the Best Paper Award at IEEE PacificVis 2018, the Best Paper Honorable Mention Award at IEEE VIS (VAST) 2018, and the Best Paper Award at IEEE VIS (SciVis) 2019.Jun Yuan is a Researcher at Tencent. His research interests lie in explainable artificial intelligence. He received his Ph.D. in Software Engineering under the supervision of Professor Shixia Liu and his B.S. degrees from Tsinghua University.

Innehållsförteckning

Introduction.- Fundamentals.- Techniques for Data Preparation.- Techniques for Model Deployment.- Research Challenges and Opportunities.- Conclusions.