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
8 Pith papers cite this work. Polarity classification is still indexing.
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Closed-loop LLM search with AST-generated examples discovers non-standard channel widths that improve vision model performance over initial architectures on CIFAR-100.
Three-example few-shot prompting optimizes LLM-generated vision architectures while a whitespace-normalized hash provides 100x faster duplicate detection than AST parsing across seven benchmarks.
NN-RAG extracts 1,289 candidate neural modules from 19 PyTorch repositories, validates 941 of them, and supplies roughly 72% of the novel structures in the LEMUR dataset while enabling cross-repository migration.
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.
FractalNet automatically generates and tests over 1,200 CNN architectures based on recursive fractal templates, achieving up to 80.18% accuracy on CIFAR-10 after five training epochs.
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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Closed-Loop LLM Discovery of Non-Standard Channel Priors in Vision Models
Closed-loop LLM search with AST-generated examples discovers non-standard channel widths that improve vision model performance over initial architectures on CIFAR-100.
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Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design
Three-example few-shot prompting optimizes LLM-generated vision architectures while a whitespace-normalized hash provides 100x faster duplicate detection than AST parsing across seven benchmarks.
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A Retrieval-Augmented Generation Approach to Extracting Algorithmic Logic from Neural Networks
NN-RAG extracts 1,289 candidate neural modules from 19 PyTorch repositories, validates 941 of them, and supplies roughly 72% of the novel structures in the LEMUR dataset while enabling cross-repository migration.
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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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Preparation of Fractal-Inspired Computational Architectures for Advanced Large Language Model Analysis
FractalNet automatically generates and tests over 1,200 CNN architectures based on recursive fractal templates, achieving up to 80.18% accuracy on CIFAR-10 after five training epochs.
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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.