【KDD 2019】Graph-based Semi-Supervised & Active Learning for Edge Flows
摘要 We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to predict the flows o
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相關文章
- 1. 【KDD 2019】A Representation Learning Framework for Property Graphs
- 2. 【Active Learning - 03】Adaptive Active Learning for Image Classification
- 3. 【Active Learning - 05】Adversarial Sampling for Active Learning
- 4. 【主動學習論文】Learning Loss for Active Learning, CVPR 2019
- 5. 【論文解讀 KDD 2019 | GATNE 】Representation Learning for Attributed Multiplex Heterogeneous Network
- 6. 【Active Learning - 01】 2013_CVPR_Adaptive Active Learning for Image Classification 論文筆記
- 7. 【KDD 2020】 Grale: Designing Networks for Graph Learning
- 8. [Machine Learning] Active Learning
- 9. 【KDD 2019】Heterogeneous Graph Neural Network
- 10. Active Learning