Learning to Learn: Meta-Critic Networks for Sample Efficient Learning||論文講解
轉載：https://zhuanlan.zhihu.com/p/27629294 文章：https://arxiv.org/abs/1706.09529 我們人是基於價值觀驅動的動物。我們做什麼不做什麼都是因爲大腦裏面有一杆秤在思考哪個更重要。即使有時候非常感性，也是因爲感性的時候做那件事的價值最大化了。既然人擁有價值觀，我們能不能讓AI也擁有價值觀，並利用價值觀來驅動AI快速學習？ 答案當然是可
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相關文章
- 1. 論文閱讀 Prototypical Networks for Few-shot Learning
- 2. 論文閱讀筆記《Meta-SGD: Learning to Learn Quickly for Few-Shot Learning》
- 3. node2vec: Scalable Feature Learning for Networks論文解讀
- 4. Learning to Learn
- 5. Matching Networks for One Shot Learning論文解讀
- 6. 《HARP-Hierarchical Representation Learning for Networks》論文解讀
- 7. 元學習Meta Learning/Learning to learn
- 8. Learning how to learn
- 9. 點雲識別-Learning to Sample
- 10. 【Learning both Weights and Connections for Efficient Neural Networks】論文筆記