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
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms
Pengyi Li, Jianye Hao, Hongyao Tang, Xian Fu, Yan Zheng, Ke Tang
Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive Survey
Zhenyu Wu, Fengmao Lv, Chenglizhao Chen, Aimin Hao, Shuo Li
Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey
Jiayuan Chen, You Shi, Changyan Yi, Hongyang Du, Jiawen Kang, Dusit Niyato
The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey
Saurav Pawar, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain, Aman Chadha, Amitava Das
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding
Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui
Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey
Bahareh Ghari, Ali Tourani, Asadollah Shahbahrami, Georgi Gaydadjiev