开课单位--新加坡国立大学 
      
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1Mixup for Node and Graph Classification[节点和图分类的混合]
				
Yiwei Wang(新加坡国立大学) Mixup for Node and Graph Classification
热度:186
Yiwei Wang(新加坡国立大学) Mixup for Node and Graph Classification
热度:186
2Fine-grained Urban Flow Prediction[细粒度城市流量预测]
				
梁宇轩(新加坡国立大学) Fine-grained Urban Flow Prediction
热度:105
梁宇轩(新加坡国立大学) Fine-grained Urban Flow Prediction
热度:105
3MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams[MSTREAM:多方面流中的快速异常检测]
				
Siddharth Bhatia(新加坡国立大学) MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams
热度:85
Siddharth Bhatia(新加坡国立大学) MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams
热度:85
4It’s Not Just the Site, it’s the Contents: Intra-domain Fingerprinting Social Media Websites Through CDN Bursts[不仅仅是网站,还有内容:通过CDN突发对社交媒体网站进行域内指纹识别]
				
Kailong Wang(新加坡国立大学) It’s Not Just the Site, it’s the Contents: Intra-domain Fingerprinting Social Media Websites Through CDN Bursts
热度:77
Kailong Wang(新加坡国立大学) It’s Not Just the Site, it’s the Contents: Intra-domain Fingerprinting Social Media Websites Through CDN Bursts
热度:77
5CurGraph: Curriculum Learning for Graph Classification[CurGraph:图形分类的课程学习]
				
Yiwei Wang(新加坡国立大学) CurGraph: Curriculum Learning for Graph Classification
热度:79
Yiwei Wang(新加坡国立大学) CurGraph: Curriculum Learning for Graph Classification
热度:79
6Learning Intents behind Interactions with Knowledge Graph for Recommendation[与推荐知识图交互后的学习意图]
				
Xiang Wang(新加坡国立大学) Learning Intents behind Interactions with Knowledge Graph for Recommendation
热度:73
Xiang Wang(新加坡国立大学) Learning Intents behind Interactions with Knowledge Graph for Recommendation
热度:73
7Feature Selection for Support Vector Regression Using Probabilistic Prediction[基于概率预测的支持向量回归特征选择]
				
Jian-Bo Yang(新加坡国立大学) This paper presents a novel wrapper-based feature selection method for Support Vector Regression (SVR) using its probabilistic predictions. The method...
热度:110
Jian-Bo Yang(新加坡国立大学) This paper presents a novel wrapper-based feature selection method for Support Vector Regression (SVR) using its probabilistic predictions. The method...
热度:110
8Modeling the Digital Camera Pipeline: From RAW to sRGB and Back[数码相机流水线的建模:从原始到sRGB再到回来]
				
Michael S. Brown(新加坡国立大学) This talk presents a study of the in-camera imaging process through an extensive analysis of more than 10,000 images from over 30 cameras. The goal is...
热度:148
Michael S. Brown(新加坡国立大学) This talk presents a study of the in-camera imaging process through an extensive analysis of more than 10,000 images from over 30 cameras. The goal is...
热度:148
9Modeling the Digital Camera Pipeline: From RAW to sRGB and Back[数码相机的管道建模:从原材料到sRGB和回]
				
Michael S. Brown(新加坡国立大学) This talk presents a study of the in-camera imaging process through an extensive analysis of more than 10,000 images from over 30 cameras. The goal is...
热度:105
Michael S. Brown(新加坡国立大学) This talk presents a study of the in-camera imaging process through an extensive analysis of more than 10,000 images from over 30 cameras. The goal is...
热度:105
10A New Texture Descriptor Using Multifractal Analysis in Multi-orientation Wavelet Pyramid[一种新的多重分形分析的多方向小波金字塔纹理描述符]
				
Hui Ji(新加坡国立大学) Based on multifractal analysis in wavelet pyramids of texture images, a new texture descriptor is proposed in this paper that implicitly combines info...
热度:102
Hui Ji(新加坡国立大学) Based on multifractal analysis in wavelet pyramids of texture images, a new texture descriptor is proposed in this paper that implicitly combines info...
热度:102