Comprehensive Taxonomy
Comprehensive taxonomies organize complex domains into hierarchical structures, aiming to clarify relationships between concepts and facilitate knowledge discovery and application. Current research focuses on developing and refining taxonomies across diverse fields, including natural language processing, computer vision, and machine learning, often leveraging large language models and advanced algorithms to automate the process and improve accuracy. These efforts are significant because well-structured taxonomies improve the efficiency of research, enhance the interpretability of complex models, and enable the development of more robust and reliable applications in various domains.
Papers
Machine Learning in Access Control: A Taxonomy and Survey
Mohammad Nur Nobi, Maanak Gupta, Lopamudra Praharaj, Mahmoud Abdelsalam, Ram Krishnan, Ravi Sandhu
FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy
André Ferreira, Jianning Li, Kelsey L. Pomykala, Jens Kleesiek, Victor Alves, Jan Egger
Taxonomy of Benchmarks in Graph Representation Learning
Renming Liu, Semih Cantürk, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek
A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester