AutoML論文筆記（十六）Neural Architecture Search without Training
文章題目：Neural Architecture Search without Training 鏈接：link https://arxiv.org/pdf/2006.04647 論文內容 論文閱讀筆記，幫助記憶的同時，方便和大家討論。因能力有限，可能有些地方理解的不到位，如有謬誤，請及時指正。 常規的NAS在訓練和單個結構驗證的過程都非常耗時，動輒幾百上千個GPU小時。本文在富含各種網絡結構的
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相關文章
- 1. NAS論文筆記——Progressive Neural Architecture Search
- 2. 論文筆記——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 3. 論文筆記：Progressive Neural Architecture Search
- 4. 論文筆記《Neural Architecture Search With Reinforcement Learning》
- 5. NAS之《Neural Architecture Transfer》論文筆記
- 6. AutoML論文筆記（八）Deep Multimodal Neural Architecture Search：多模態神經網絡搜索
- 7. 論文筆記：Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
- 8. automl-演化算法-Regularized evolution for image classifier architecture search-論文筆記
- 9. 【論文筆記】Efficient Neural Architecture Search via Parameter Sharing
- 10. 【譯】An Opinionated Introduction to AutoML and Neural Architecture Search