Tutorial Review
Tutorial reviews synthesize existing research to provide comprehensive introductions to specific topics within a field, aiming to improve accessibility and understanding for a broader audience. Current research focuses on diverse areas, including the development of rigorous methodologies for metaheuristic algorithms, the evaluation of large language models for complex tasks like multi-document question answering and skill composition, and the application of advanced techniques like diffusion models for image enhancement and physics-informed neural networks for quantum system analysis. These tutorials play a crucial role in disseminating knowledge, fostering collaboration, and accelerating progress across various scientific disciplines and practical applications.
Papers
SE(3)-Equivariant Robot Learning and Control: A Tutorial Survey
Joohwan Seo, Soochul Yoo, Junwoo Chang, Hyunseok An, Hyunwoo Ryu, Soomi Lee, Arvind Kruthiventy, Jongeun CHoi, Roberto HorowitzBerkeley●Yonsei University●MITFoundation Models for Spatio-Temporal Data Science: A Tutorial and Survey
Yuxuan Liang, Haomin Wen, Yutong Xia, Ming Jin, Bin Yang, Flora Salim, Qingsong Wen, Shirui Pan, Gao Cong
Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
Yuxuan Du, Xinbiao Wang, Naixu Guo, Zhan Yu, Yang Qian, Kaining Zhang, Min-Hsiu Hsieh, Patrick Rebentrost, Dacheng TaoTutorial on Using Machine Learning and Deep Learning Models for Mental Illness Detection
Yeyubei Zhang, Zhongyan Wang, Zhanyi Ding, Yexin Tian, Jianglai Dai, Xiaorui Shen, Yunchong Liu, Yuchen Cao