AD-Aligning: Emulating Human-like Generalization for Cognitive Domain Adaptation in Deep Learning

Autor: Li, Zhuoying, Wan, Bohua, Mu, Cong, Zhao, Ruzhang, Qiu, Shushan, Yan, Chao

Domain adaptation is pivotal for enabling deep learning models to generalize across diverse domains, a task complicated by variations in presentation and cognitive nuances. In this paper, we introduce AD-Aligning, a novel approach that combines adver

Externí odkaz: http://arxiv.org/abs/2405.09582

Dyadic relational graph convolutional networks for skeleton-based human interaction recognition

Autor: Zhu, Liping, Wan, Bohua, Li, Chengyang, Tian, Gangyi, Hou, Yi, Yuan, Kun

Publikováno v: In Pattern Recognition July 2021 115

A Unified Framework on Generalizability of Clinical Prediction Models.

Autor: Wan B; Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States., Caffo B; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.; Malone Center for Engineering in Healthcare, Whiting School of Engineering, Baltimore, MD, United States., Vedula SS; Malone Center for Engineering in Healthcare, Whiting School of Engineering, Baltimore, MD, United States.

Publikováno v: Frontiers in artificial intelligence [Front Artif Intell] 2022 Apr 29; Vol. 5, pp. 872720. Date of Electronic Publication: 2022 Apr 29 (Print Publication: 2022).