DeepLearning論文閱讀筆記(一):Cyclical Learning Rates for Training Neural Networks(CLR)
論文鏈接 論文內容關鍵在於兩點： 1.CLR提出了一種在神經網絡訓練中設置global learning rates的方法，用來解決手動實驗去尋找最優學習率的問題，不需要額外的計算，且通常需要更少的迭代次數。它就是讓學習率在迭代過程中週期性變化，而不是固定的值。 2.如何去估計CLR中的超參，如週期長度(cycle length)和邊界值(boundary values)。 1.Cyclica
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相關文章
- 1. 1506.01186-Cyclical Learning Rates for Training Neural Networks
- 2. learning rate對深度模型的影響：論文閱讀Cyclical Learning Rates for Training Neural Networks
- 3. Machine Learning & Deep Learning 論文閱讀筆記
- 4. 論文閱讀筆記《Meta-Learning of Neural Architectures for Few-Shot Learning》
- 5. 論文閱讀筆記（一）：Learning Dual Convolutional Neural Networks for Low-Level Vision
- 6. 論文《Learning both Weights and Connections for Efficient Neural Network》閱讀筆記
- 7. 論文閱讀筆記《Edge-Labeling Graph Neural Network for Few-shot Learning》
- 8. 論文閱讀：Bag of Freebies for Training Object Detection Neural Networks
- 9. Global Sparse Momentum SGD for Pruning Very Deep Neural Networks 論文閱讀筆記
- 10. 論文閱讀 Prototypical Networks for Few-shot Learning