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Found 2 papers, 1 papers with code
Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach
Although evolutionary-based NAS works were reported to require a considerable amount of GPU time for architecture search, our approach obtained promising results in little time, encouraging further experiments in evolutionary-based NAS, for search and network representation improvements.
Ranked #11 on
Neural Architecture Search
on CIFAR-100
Extracting Lungs from CT Images using Fully Convolutional Networks
Dice scores of $98. 67\%\pm0. 94\%$ for the HUG-ILD dataset and $99. 19\%\pm0. 37\%$ for the VESSEL12 dataset were achieved, outperforming works in the former and obtaining similar state-of-the-art results in the latter dataset, showing the capability in using deep learning approaches.
