Fine-tuned 7B LLMs generating unified diffs for neural architecture refinement achieve 66-75% valid rates and 64-66% mean first-epoch accuracy, outperforming full-generation baselines by large margins while cutting output length by 75-85%.
LEMUR neural net- work dataset: Towards seamless AutoML.arXiv preprint
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Fine-tuned LLMs reach 80% accuracy predicting which dataset a neural network code performs better on, outperforming metadata prompts at 70%.
A 1.96M-parameter LiteDenoiseNet student model achieves 37.58 dB PSNR on full-resolution real image denoising benchmarks while running in 34-46 ms on mobile NPUs by leveraging NPU-compatible primitives and high-alpha knowledge distillation.
MobileAgeNet uses a MobileNetV3-Large backbone with a regression head to achieve 4.65 years mean absolute error in age estimation and 14.4 ms on-device latency with 3.23 million parameters.
citing papers explorer
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Delta-Based Neural Architecture Search: LLM Fine-Tuning via Code Diffs
Fine-tuned 7B LLMs generating unified diffs for neural architecture refinement achieve 66-75% valid rates and 64-66% mean first-epoch accuracy, outperforming full-generation baselines by large margins while cutting output length by 75-85%.
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From Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT
Fine-tuned LLMs reach 80% accuracy predicting which dataset a neural network code performs better on, outperforming metadata prompts at 70%.
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Real Image Denoising with Knowledge Distillation for High-Performance Mobile NPUs
A 1.96M-parameter LiteDenoiseNet student model achieves 37.58 dB PSNR on full-resolution real image denoising benchmarks while running in 34-46 ms on mobile NPUs by leveraging NPU-compatible primitives and high-alpha knowledge distillation.
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MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment
MobileAgeNet uses a MobileNetV3-Large backbone with a regression head to achieve 4.65 years mean absolute error in age estimation and 14.4 ms on-device latency with 3.23 million parameters.