元圖Meta-Graph: Few shot link prediction via meta learning理解與部分翻譯
引言 鏈接預測的目的是能夠從圖中學習並推測出新的或者未知的關係。比如說，從社交網絡方面，通過鏈接預測構建一個朋友推薦系統，從生物網絡方面，通過鏈接預測推斷出藥物、蛋白質和疾病直接的關係。但是這些工作只聚焦於單一的問題設置：鏈接預測都用於一個單一的大圖，並且這個圖中的關係完整，至少有50%的真實鏈接（邊）可以在訓練階段被看到。 本篇文章要做是基於小樣本鏈接預測這麼一個更有挑戰性的設定，目標是在多個只
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相關文章
- 1. Meta-SGD: Learning to learn quickly for few shot learning---翻譯理解
- 2. Few-shot Learning with Meta Metric Learners
- 3. Graph Few-shot learning via Knowledge Transfer
- 4. few-shot learning, zero-shot learning, one-shot learning,any-shot learning, C-way K-shot，Meta-learn
- 5. 小樣本學習（Few-shot Learning）與元學習(meta-learning)初步
- 6. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS翻譯
- 7. 翻譯-Neural Snowball for Few-Shot Relation Learning
- 8. 元學習與小樣本學習理解 Meta learning and Few shot learning
- 9. 【論文閱讀】[meta learning]cross-domain few-shot classification via learned feature-wise transformation.
- 10. Few-shot Object Detection via Feature Reweighting