52 citations · 121 across the 23 of their papers we have counts for
6 papers · 1 filter
WOOD: Wasserstein-based Out-of-Distribution Detection
Yinan Wang, Wenbo Sun, Jionghua "Judy" Jin +2
The training and test data for deep-neural-network-based classifiers are usually assumed to be sampled from the same distribution. When part of the test samples are drawn from a di…
Nested Bayesian Optimization for Computer Experiments
Yan Wang, Meng Wang, Areej AlBahar +1
Computer experiments can emulate the physical systems, help computational investigations, and yield analytic solutions. They have been widely employed with many engineering applica…
Failure-averse Active Learning for Physics-constrained Systems
Cheolhei Lee, Xing Wang, Jianguo Wu +1
Active learning is a subfield of machine learning that is devised for design and modeling of systems with highly expensive sampling costs. Industrial and engineering systems are ge…
A Robust Asymmetric Kernel Function for Bayesian Optimization, with Application to Image Defect Detection in Manufacturing Systems
Areej AlBahar, Inyoung Kim, Xiaowei Yue
Some response surface functions in complex engineering systems are usually highly nonlinear, unformed, and expensive-to-evaluate. To tackle this challenge, Bayesian optimization, w…
Task Allocation and Coordinated Motion Planning for Autonomous Multi-Robot Optical Inspection Systems
Yinhua Liu, Wenzheng Zhao, Tim Lutz +1
Autonomous multi-robot optical inspection systems are increasingly applied for obtaining inline measurements in process monitoring and quality control. Numerous methods for path pl…
Partitioned Active Learning for Heterogeneous Systems
Cheolhei Lee, Kaiwen Wang, Jianguo Wu +2
Active learning is a subfield of machine learning that focuses on improving the data collection efficiency of expensive-to-evaluate systems. Especially, active learning integrated…