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This paper introduces surrogate benchmarks for Neural Architecture Search (NAS) to address the limitations of existing tabular benchmarks, which are constrained to small search spaces. The proposed methodology allows for the creation of cost-effective surrogate benchmarks for larger search spaces, demonstrating better performance modeling and insights compared to traditional methods. The authors open-source their code and data, emphasizing the importance of these surrogate benchmarks for advancing NAS resea

Author

Phát Lê Phước

Language

EN