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Found 4 papers, 1 papers with code
A First Step Towards Runtime Analysis of Evolutionary Neural Architecture Search
Evolutionary neural architecture search (ENAS) employs evolutionary algorithms to find high-performing neural architectures automatically, and has achieved great success.
Efficient Evaluation Methods for Neural Architecture Search: A Survey
In surveying each category, we further discuss the design principles and analyze the strength and weaknesses to clarify the landscape of existing EEMs, thus making easily understanding the research trends of EEMs.
Bridge the Gap Between Architecture Spaces via A Cross-Domain Predictor
To solve this issue, we propose a Cross-Domain Predictor (CDP), which is trained based on the existing NAS benchmark datasets (e. g., NAS-Bench-101), but can be used to find high-performance architectures in large-scale search spaces.
Analyzing the Expected Hitting Time of Evolutionary Computation-based Neural Architecture Search Algorithms
Evolutionary computation-based neural architecture search (ENAS) is a popular technique for automating architecture design of deep neural networks.
