12 citations · 17 across the 22 of their papers we have counts for
10 papers · 1 filter
Optimal Labeler Assignment and Sampling for Active Learning in the Presence of Imperfect Labels
Pouya Ahadi, Blair Winograd, Camille Zaug +3
Active Learning (AL) has garnered significant interest across various application domains where labeling training data is costly. AL provides a framework that helps practitioners q…
Bayesian Optimization for Function-Valued Responses under Min-Max Criteria
Pouya Ahadi, Reza Marzban, Ali Adibi +1
Bayesian optimization is widely used for optimizing expensive black box functions, but most existing approaches focus on scalar responses. In many scientific and engineering settin…
Teaching LLMs to See and Guide: Context-Aware Real-Time Assistance in Augmented Reality
Mahya Qorbani, Kamran Paynabar, Mohsen Moghaddam
The growing adoption of augmented and virtual reality (AR and VR) technologies in industrial training and on-the-job assistance has created new opportunities for intelligent, conte…
An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity
Junghee Pyeon, Davide Cacciarelli, Kamran Paynabar
Concept drift and label scarcity are two critical challenges limiting the robustness of predictive models in dynamic industrial environments. Existing drift detection methods often…
Registration-Free Monitoring of Unstructured Point Cloud Data via Intrinsic Geometrical Properties
Mariafrancesca Patalano, Giovanna Capizzi, Kamran Paynabar
Modern sensing technologies have enabled the collection of unstructured point cloud data (PCD) of varying sizes, which are used to monitor the geometric accuracy of 3D objects. PCD…
Tensor-on-tensor Regression Neural Networks for Process Modeling with High-dimensional Data
Qian Wang, Mohammad N. Bisheh, Kamran Paynabar
Modern sensing and metrology systems now stream terabytes of heterogeneous, high-dimensional (HD) data profiles, images, and dense point clouds, whose natural representation is mul…