Meta-SGD: Learning to learn quickly for few shot learning---翻譯理解
Abstract 對於孤立地從頭開始學習每個任務的算法來說，少鏡頭學習是一個挑戰。相反，元學習從許多相關的任務中學習，元學習者可以用更少的例子更準確、更快地學習新任務，而元學習者的選擇是至關重要的。在這篇論文中，我們開發了一種類似於sgd的、易於訓練的元學習器，它可以在監督學習和強化學習上一步初始化和適應任何可微分的學習者。與流行的元學習器LSTM相比，元sgd在概念上更簡單，更容易實現，並且可以
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- 1. 論文閱讀筆記《Meta-SGD: Learning to Learn Quickly for Few-Shot Learning》
- 2. 翻譯-Neural Snowball for Few-Shot Relation Learning
- 3. Zero-shot Learning / One-shot Learning / Few-shot Learning
- 4. few-shot learning, zero-shot learning, one-shot learning,any-shot learning, C-way K-shot，Meta-learn
- 5. Prototypical Networks for Few-shot Learning
- 6. Few-shot-learning
- 7. Zero-shot Learning / One-shot Learning / Few-shot Learning/傳統 Learning
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相關文章
- 1. 論文閱讀筆記《Meta-SGD: Learning to Learn Quickly for Few-Shot Learning》
- 2. 翻譯-Neural Snowball for Few-Shot Relation Learning
- 3. Zero-shot Learning / One-shot Learning / Few-shot Learning
- 4. few-shot learning, zero-shot learning, one-shot learning,any-shot learning, C-way K-shot，Meta-learn
- 5. Prototypical Networks for Few-shot Learning
- 6. Few-shot-learning
- 7. Zero-shot Learning / One-shot Learning / Few-shot Learning/傳統 Learning
- 8. Zero-shot / One-shot / Few-shot Learning
- 9. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS翻譯
- 10. Cross-Modulation Networks For Few-Shot Learning