Code-Oriented LM Embeddings (COLE) extracted from raw PyTorch class text with frozen language models improve surrogate predictors in NAS, cutting evaluation budget by 34% in BANANAS on NAS-Bench-201 for CIFAR-100.
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Behavior latticing synthesizes connections across unstructured user interactions to generate insights into underlying motivations, yielding deeper and more accurate user understanding than task-only models.
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Towards Code-Oriented LM Embeddings for Surrogate-Assisted Neural Architecture Search
Code-Oriented LM Embeddings (COLE) extracted from raw PyTorch class text with frozen language models improve surrogate predictors in NAS, cutting evaluation budget by 34% in BANANAS on NAS-Bench-201 for CIFAR-100.
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Behavior Latticing: Inferring User Motivations from Unstructured Interactions
Behavior latticing synthesizes connections across unstructured user interactions to generate insights into underlying motivations, yielding deeper and more accurate user understanding than task-only models.