March 13, 2024, 4:42 a.m. | Juan Zou, Han Chu, Yizhang Xia, Junwen Xu, Yuan Liu, Zhanglu Hou

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07035v1 Announce Type: cross
Abstract: The effectiveness of Evolutionary Neural Architecture Search (ENAS) is influenced by the design of the search space. Nevertheless, common methods including the global search space, scalable search space and hierarchical search space have certain limitations. Specifically, the global search space requires a significant amount of computational resources and time, the scalable search space sacrifices the diversity of network structures and the hierarchical search space increases the search cost in exchange for network diversity. To address …

abstract architecture arxiv computational cs.lg cs.ne design evolution global hierarchical limitations multiple neural architecture search population resources scalable search space 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

Senior Data Science Analyst- ML/DL/LLM

@ Mayo Clinic | Jacksonville, FL, United States

Machine Learning Research Scientist, Robustness and Uncertainty

@ Nuro, Inc. | Mountain View, California (HQ)