Code embeddings boost neural architecture search efficiency

Researchers have developed a novel method called Code-Oriented LM Embeddings (COLE) to improve Neural Architecture Search (NAS). This technique uses off-the-shelf language models to generate embeddings from code representations of neural architectures, bypassing the need for expensive fine-tuning or complex feature engineering. Experiments on NAS-Bench-201 and einspace demonstrated that COLE embeddings outperform other text-based encodings and significantly reduce the evaluation budget required to find high-performing architectures. AI

IMPACT Introduces a more efficient method for designing neural networks, potentially accelerating AI model development.

RANK_REASON The cluster contains an academic paper detailing a new methodology for neural architecture search. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

• CIFAR-100
• COLE
• einspace
• Language Models
• NAS-Bench-201
• Neural Architecture Search
• PyTorch

• paper
• other

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