开课单位--亚利桑那州立大学
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Is the Whole Greater Than the Sum of Its Parts?[整体大于部分之和吗?
   Liangyue Li(亚利桑那州立大学) The part-whole relationship routinely finds itself in many disciplines, ranging from collaborative teams, crowdsourcing, autonomous systems to network...
热度:11

2
A Local Algorithm for Structure­Preserving Graph Cut[一种保结构图割的局部算法]
  周大伟(亚利桑那州立大学) Nowadays, large-scale graph data is being generated in a variety of real-world applications, from social networks to co-authorship networks, from prot...
热度:16

3
FASTEN: Fast Sylvester Equation Solver for Graph Mining[FASTEN:用于图形挖掘的快速Sylvester方程求解器]
  Boxin Du(亚利桑那州立大学) The Sylvester equation offers a powerful and unifying primitive for a variety of important graph mining tasks, including network alignment, graph kern...
热度:26

4
Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners[释放你所学到的:指数衰退记忆学习者的适应性群体教学]
  Yao Zhou(亚利桑那州立大学) With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications. However, most e...
热度:27

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Fast Best-Effort Pattern Matching in Large Attributed Graphs[ 大型属性图的快速尽力而为模式匹配 ]
  Hanghang Tong(亚利桑那州立大学) We focus on large graphs where nodes have attributes, such as a social network where the nodes are labelled with each person’s job title. In suc...
热度:68

6
Connecting Users across Social Media Sites: A Behavioral-Modeling Approach[跨社交媒体网站连接用户:一种行为建模方法]
   Reza Zafarani(亚利桑那州立大学) People use various social media for different purposes. The information on an individual site is often incomplete. When sources of complementary infor...
热度:32

7
Fast Best-Effort Pattern Matching in Large Attributed Graphs[在大归因图快速尽力而为模式匹配]
  Hanghang Tong(亚利桑那州立大学) We focus on large graphs where nodes have attributes, such as a social network where the nodes are labelled with each person’s job title. In suc...
热度:44

9
Learning Incoherent Sparse and Low-Rank Patterns from Multiple Tasks[学习从多个任务不相干的稀疏和低秩的模式]
  Jianhui Chen(亚利桑那州立大学) We consider the problem of learning incoherent sparse and low-rank patterns from multiple tasks. Our approach is based on a linear multi-task learning...
热度:37

10
Human-Centered Machine Learning in a Social Interaction Assistant for Individuals with Visual Impairments[为了有视觉障碍的人而建立一个以人为本的机器学习社会互动平台]
  Shayok Chakraborty(亚利桑那州立大学) Over the last couple of decades, the increasing focus on accessibility has resulted in the design and development of several assistive technologies to...
热度:35
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