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
Developing a Taxonomy of Elements Adversarial to Autonomous Vehicles
Mohammadali Saffary, Nishan Inampudi, Joshua E. Siegel
Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy, Survey and Insights
Yuan Wang, Lokesh Kumar Sambasivan, Mingang Fu, Prakhar Mehrotra
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
Xingchen Zou, Yibo Yan, Xixuan Hao, Yuehong Hu, Haomin Wen, Erdong Liu, Junbo Zhang, Yong Li, Tianrui Li, Yu Zheng, Yuxuan Liang