Comprehensive Survey
Comprehensive surveys in various scientific fields systematically review existing research, aiming to synthesize key findings, identify gaps, and guide future directions. Current research focuses on evaluating and improving the trustworthiness, efficiency, and bias mitigation of models across diverse domains, including large language models, image generation, and autonomous systems. These surveys are crucial for advancing understanding within specific subfields and facilitating the development of more robust and reliable technologies with broader practical applications.
Papers
Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques
Saidul Islam, Gaith Rjoub, Hanae Elmekki, Jamal Bentahar, Witold Pedrycz, Robin Cohen
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
Xiangzhong Luo, Di Liu, Hao Kong, Shuo Huai, Hui Chen, Guochu Xiong, Weichen Liu
A Systematic Survey on Instructional Text: From Representation and Downstream NLP Tasks
Abdulfattah Safa, Tamta Kapanadze, Arda Uzunoğlu, Gözde Gül Şahin
Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey
Kun Shi, Shibo He, Zhenyu Shi, Anjun Chen, Zehui Xiong, Jiming Chen, Jun Luo
Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey
Xinyu Wang, Wenbo Zhang, Sarah Rajtmajer
A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning
Jingbo Zhang, Qiong Wu, Pingyi Fan, Qiang Fan
A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence
Louise McCormack, Malika Bendechache
Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey
Zihan Yu, Tianxiao Li, Yuxin Zhu, Rongze Pan