Learning from the Past: Continaul Meta-Learning with Bayesian Graph Neural network review
1. 提出問題 現有的基於圖網絡做meta-learning的方法會有Catastrophic forgetting 和 insufficient robustness的問題， 導致不嗯呢個保持long-term的knowledge，並會造成嚴重的錯誤積累。 Catastraphic forgetting insufficient robustness 2. 提出解決方案 針對Catastraph
相關文章
- 1. Review on The Graph Neural Network Model
- 2. Graph Neural Network Review
- 3. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORK
- 4. Few-Shot Learning with Graph Neural Networks
- 5. Learning about Graph Network
- 6. 3 The Bayesian Network Representation
- 7. Few-shot Learning with Graph Neural Networks
- 8. 《The graph neural network model》筆記
- 9. Fast deep reinforcement learning using online adjustments from the past
- 10. 【KDD 2019】Heterogeneous Graph Neural Network
- 更多相關文章...
- • XSLT
- • PHP header() 函數 - PHP參考手冊
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
- • Java Agent入門實戰（三）-JVM Attach原理與使用
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Review on The Graph Neural Network Model
- 2. Graph Neural Network Review
- 3. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORK
- 4. Few-Shot Learning with Graph Neural Networks
- 5. Learning about Graph Network
- 6. 3 The Bayesian Network Representation
- 7. Few-shot Learning with Graph Neural Networks
- 8. 《The graph neural network model》筆記
- 9. Fast deep reinforcement learning using online adjustments from the past
- 10. 【KDD 2019】Heterogeneous Graph Neural Network