April 19, 2024, 4:43 a.m. | Dingzhu Wen, Xiaoyang Li, Yong Zhou, Yuanming Shi, Sheng Wu, Chunxiao Jiang

cs.LG updates on arXiv.org arxiv.org

arXiv:2306.01162v2 Announce Type: replace-cross
Abstract: Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything. The performance of edge AI tasks, including edge learning and edge AI inference, depends on the quality of three highly coupled processes, i.e., sensing for data acquisition, computation for information extraction, and communication for information transmission. However, these three modules need to …

abstract advanced and edge ai artificial artificial intelligence arxiv auto communication communications computation cs.ai cs.it cs.lg digital digital twins driving edge edge ai everything inference intelligence math.it performance projection semantic sensing series solution tasks twins type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Data Scientist

@ Publicis Groupe | New York City, United States

Bigdata Cloud Developer - Spark - Assistant Manager

@ State Street | Hyderabad, India