D
Duen Horng Chau
Researcher at Georgia Institute of Technology
Publications - 181
Citations - 6012
Duen Horng Chau is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Visualization & Interactive visualization. The author has an hindex of 36, co-authored 181 publications receiving 4837 citations. Previous affiliations of Duen Horng Chau include Symantec & Carnegie Mellon University.
Papers
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Journal ArticleDOI
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
TL;DR: A survey of the role of visual analytics in deep learning research is presented, which highlights its short yet impactful history and thoroughly summarizes the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How.
Proceedings ArticleDOI
Netprobe: a fast and scalable system for fraud detection in online auction networks
TL;DR: This paper describes the design and implementation of NetProbe, a system that is a Markov Random Field tuned to detect the suspicious patterns that fraudsters create, and employs a Belief Propagation mechanism to detect likely fraudsters.
Posted Content
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
Nilaksh Das,Madhuri Shanbhogue,Shang-Tse Chen,Fred Hohman,Li Chen,Michael E. Kounavis,Duen Horng Chau +6 more
TL;DR: This work explores and demonstrates how systematic JPEG compression can work as an effective pre-processing step in the classification pipeline to counter adversarial attacks and dramatically reduce their effects, and proposes an ensemble-based technique that can be constructed quickly from a given well-performing DNN.
Book ChapterDOI
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
TL;DR: Shang et al. as discussed by the authors proposed ShapeShifter, an attack that tackles the more challenging problem of crafting physical adversarial perturbations to fool image-based object detectors like Faster R-CNN.
Proceedings ArticleDOI
Apolo: making sense of large network data by combining rich user interaction and machine learning
TL;DR: This work introduces Apolo, a system that uses a mixed-initiative approach - combining visualization, rich user interaction and machine learning - to guide the user to incrementally and interactively explore large network data and make sense of it.