图 书 馆开课单位--加利福尼亚大学
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41Model Reduction for Parameter Estimation[参数估计的模型简化]
Eric Mjolsness(加利福尼亚大学) Estimating parameters in biochemical network models is a central but often difficult problem. A general approach that may be worth developing further ...
热度：53
Eric Mjolsness(加利福尼亚大学) Estimating parameters in biochemical network models is a central but often difficult problem. A general approach that may be worth developing further ...
热度：53
42Continuous-Time Regression Models for Longitudinal Networks[纵向网络的连续时间回归模型]
Arthur Asuncion(加利福尼亚大学) The development of statistical models for continuous-time longitudinal network data is of increasing interest in machine learning and social science. ...
热度：53
Arthur Asuncion(加利福尼亚大学) The development of statistical models for continuous-time longitudinal network data is of increasing interest in machine learning and social science. ...
热度：53
43High-Dimensional Graphical Model Selection[高维图形模型选择]
Animashree Anandkumar(加利福尼亚大学) We consider the problem of Ising and Gaussian graphical model selection given n i.i.d. samples from the model. We propose an efficient threshold-based...
热度：45
Animashree Anandkumar(加利福尼亚大学) We consider the problem of Ising and Gaussian graphical model selection given n i.i.d. samples from the model. We propose an efficient threshold-based...
热度：45
44Vision-Based Control, Control-Based Vision, and the Information Knot That Ties Them [基于视觉的控制、基于控制的视觉以及将它们联系起来的信息结]
Stefano Soatto(加利福尼亚大学) The purpose of this tutorial is to explore the interplay between sensing and control, to highlight the "information knot" that ties them, an...
热度：36
Stefano Soatto(加利福尼亚大学) The purpose of this tutorial is to explore the interplay between sensing and control, to highlight the "information knot" that ties them, an...
热度：36
45Learning concept graphs from text with stick-breaking priors[从前所未有的文本中学习概念图]
America Chambers(加利福尼亚大学) We present a generative probabilistic model for learning general graph structures, which we term concept graphs, from text. Concept graphs provide a v...
热度：59
America Chambers(加利福尼亚大学) We present a generative probabilistic model for learning general graph structures, which we term concept graphs, from text. Concept graphs provide a v...
热度：59
46Global and Regional Climate Change: Underlying Science and Emerging Riddles[全球和区域气候变化：潜在的科学和新兴的谜语]
Veerabhadran Ramanathan(加利福尼亚大学) Veerabhadran Ramanathan recaps 35 years of key findings, and brings his audience up to date on the latest climate data, models, and observations which...
热度：66
Veerabhadran Ramanathan(加利福尼亚大学) Veerabhadran Ramanathan recaps 35 years of key findings, and brings his audience up to date on the latest climate data, models, and observations which...
热度：66
47The Second Law and Biophysics[第二定律与生物物理学]
Kenneth Dill(加利福尼亚大学) “Biology is messy,” says Kenneth Dill, and it’s “heavily about entropy.” Just look at how biological systems repeat entr...
热度：87
Kenneth Dill(加利福尼亚大学) “Biology is messy,” says Kenneth Dill, and it’s “heavily about entropy.” Just look at how biological systems repeat entr...
热度：87
48Topic Dynamics: An Alternative Model of Bursts in Streams of Topics[主题动力学：主题流中突发事件的替代模型 ]
Dan He(加利福尼亚大学) For some time there has been increasing interest in the problem of monitoring the occurrence of topics in a stream of events, such as a stream of news...
热度：71
Dan He(加利福尼亚大学) For some time there has been increasing interest in the problem of monitoring the occurrence of topics in a stream of events, such as a stream of news...
热度：71
49Finding a Team of Experts in Social Networks[在社交网络中寻找专家团队 ]
Theodoros Lappas(加利福尼亚大学) Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we stud...
热度：58
Theodoros Lappas(加利福尼亚大学) Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we stud...
热度：58
50A Principled and Flexible Framework for Finding Alternative Clusterings[一个原则性的、灵活的寻找替代集群的框架 ]
Ian Davidson(加利福尼亚大学) The aim of data mining is to find novel and actionable insights in data. However, most algorithms typically just find a single (possibly non-novel/act...
热度：24
Ian Davidson(加利福尼亚大学) The aim of data mining is to find novel and actionable insights in data. However, most algorithms typically just find a single (possibly non-novel/act...
热度：24