EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection
論文傳送門 作者 香港科技大學 Quan Li Kristanto Sean Njotoprawiro Hammad Haleem Xiaojuan Ma 騰訊微信 Qiaoan Chen Chris Yi 摘要 通過嵌入模型爲網絡中的節點構造潛在向量已經在許多圖分析應用程序中表明瞭其實用性，例如節點分類、聚類和鏈接預測。然而，儘管嵌入學習模型的效率和準確性高，但是人們幾乎不知道在嵌入向量中保留了
相關文章
- 1. A Deep Neural Network Approach To Speech Bandwidth Expansion
- 2. A Survey of Network Embedding
- 3. A differentiable approach to inductive logic programming
- 4. Many-to-many Cross-lingual Voice Conversion with a Jointly Trained Speaker Embedding Network
- 5. Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
- 6. Content to Node: Self-Translation Network Embedding
- 7. [譯]A Bayesian Approach to Digital Matting
- 8. A Robust Adversarial Training Approach to Machine Reading Comprehension
- 9. Ajax: A New Approach to Web Applications
- 10. A holistic approach to semi-supervised learning
- 更多相關文章...
- • ASP.NET Razor - 標記 - ASP.NET 教程
- • ASP.NET Web Pages - 更多幫助器 - ASP.NET 教程
- • Flink 數據傳輸及反壓詳解
- • 委託模式
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
- 1. python的安裝和Hello，World編寫
- 2. 重磅解讀：K8s Cluster Autoscaler模塊及對應華爲雲插件Deep Dive
- 3. 鴻蒙學習筆記2（永不斷更）
- 4. static關鍵字 和構造代碼塊
- 5. JVM筆記
- 6. 無法啓動 C/C++ 語言服務器。IntelliSense 功能將被禁用。錯誤: Missing binary at c:\Users\MSI-NB\.vscode\extensions\ms-vsc
- 7. 【Hive】Hive返回碼狀態含義
- 8. Java樹形結構遞歸（以時間換空間）和非遞歸（以空間換時間）
- 9. 數據預處理---缺失值
- 10. 都要2021年了，現代C++有什麼值得我們學習的？
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. A Deep Neural Network Approach To Speech Bandwidth Expansion
- 2. A Survey of Network Embedding
- 3. A differentiable approach to inductive logic programming
- 4. Many-to-many Cross-lingual Voice Conversion with a Jointly Trained Speaker Embedding Network
- 5. Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
- 6. Content to Node: Self-Translation Network Embedding
- 7. [譯]A Bayesian Approach to Digital Matting
- 8. A Robust Adversarial Training Approach to Machine Reading Comprehension
- 9. Ajax: A New Approach to Web Applications
- 10. A holistic approach to semi-supervised learning