The paper derives a posteriori error estimates for neural network depth adaptation by formulating training as an optimal control problem and using dual weighted residuals to insert layers where error is highest.
Random search and reproducibility for neural architecture search
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CoLLM-NAS introduces a collaborative two-LLM framework with Navigator, Generator, and Coordinator modules to perform knowledge-guided neural architecture search, reporting state-of-the-art results on ImageNet and NAS-Bench-201 with 4-10x lower search cost.
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An optimal control approach for neural network architecture adaptation with a posteriori error estimation
The paper derives a posteriori error estimates for neural network depth adaptation by formulating training as an optimal control problem and using dual weighted residuals to insert layers where error is highest.
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CoLLM-NAS: Collaborative Large Language Models for Efficient Knowledge-Guided Neural Architecture Search
CoLLM-NAS introduces a collaborative two-LLM framework with Navigator, Generator, and Coordinator modules to perform knowledge-guided neural architecture search, reporting state-of-the-art results on ImageNet and NAS-Bench-201 with 4-10x lower search cost.