图 书 馆开课单位--卡内基梅隆大学
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131Duolingo: Translating the Web with Millions of People[Duolingo：数以百万计的人翻译网站]
Luis von Ahn(卡内基梅隆大学) I want to translate the web into every major language: every web page, every video, and, yes, even Justin Bieber's tweets. With its content split ...
热度：76
Luis von Ahn(卡内基梅隆大学) I want to translate the web into every major language: every web page, every video, and, yes, even Justin Bieber's tweets. With its content split ...
热度：76
132TopicFlow Model: Unsupervised Learning of Topic-specific Influences of Hyperlinked Documents[Topicflow模型：对超链接文档主题影响的无监督学习]
Ramesh Nallapati(卡内基梅隆大学) Popular algorithms for modeling the influence of entities in networked data, such as PageRank, work by analyzing the hyperlink structure, but ignore t...
热度：188
Ramesh Nallapati(卡内基梅隆大学) Popular algorithms for modeling the influence of entities in networked data, such as PageRank, work by analyzing the hyperlink structure, but ignore t...
热度：188
133Classification in Very High Dimensional Problems with Handfuls of Examples[在一把一把的例子非常高维问题的分类]
Mark Palatucci(卡内基梅隆大学)
热度：54
Mark Palatucci(卡内基梅隆大学)
热度：54
134Graph-Valued Regression[图的价值回归]
Xi Chen(卡内基梅隆大学) Undirected graphical models encode in a graph G the dependency structure of a random vector Y. In many applications, it is of interest to model Y give...
热度：41
Xi Chen(卡内基梅隆大学) Undirected graphical models encode in a graph G the dependency structure of a random vector Y. In many applications, it is of interest to model Y give...
热度：41
135Identifying graph-structured activation patterns in networks[网络中图形结构激活模式的识别]
James Sharpnack(卡内基梅隆大学) We consider the problem of identifying an activation pattern in a complex, large-scale network that is embedded in very noisy measurements. This probl...
热度：34
James Sharpnack(卡内基梅隆大学) We consider the problem of identifying an activation pattern in a complex, large-scale network that is embedded in very noisy measurements. This probl...
热度：34
136Online Learning by Ellipsoid Method[椭球法在线学习]
Liu Yang(卡内基梅隆大学) In this work, we extend the ellipsoid method, which was originally designed for convex optimization, for online learning. The key idea is to approxima...
热度：344
Liu Yang(卡内基梅隆大学) In this work, we extend the ellipsoid method, which was originally designed for convex optimization, for online learning. The key idea is to approxima...
热度：344
137Imitation Learning and Purposeful Prediction: Probabilistic and Non-probabilistic Methods [模仿学习和有意义的预测：概率和非概率方法]
Drew Bagnell(卡内基梅隆大学) 编程机器人的行为仍然是一个具有挑战性的任务。虽然它往往是简单的抽象定义甚至显示所需的行为，设计一个控制器，具有相同的行为是困难的，费时和昂贵的，最终的...
热度：49
Drew Bagnell(卡内基梅隆大学) 编程机器人的行为仍然是一个具有挑战性的任务。虽然它往往是简单的抽象定义甚至显示所需的行为，设计一个控制器，具有相同的行为是困难的，费时和昂贵的，最终的...
热度：49
138Semisupervised Learning Approaches[半监督学习方法]
Tom Mitchell(卡内基梅隆大学) Categories Top » Computer Science » Machine Learning Top » Computer Science » Machine Learning » Semi-supervised Learn...
热度：48
Tom Mitchell(卡内基梅隆大学) Categories Top » Computer Science » Machine Learning Top » Computer Science » Machine Learning » Semi-supervised Learn...
热度：48
139Multiscale Topic Tomography[多尺度的主题摄影]
Ramesh Nallapati(卡内基梅隆大学) Modeling the evolution of topics with time is of great value in automatic summarization and analysis of large document collections. In this work, we p...
热度：34
Ramesh Nallapati(卡内基梅隆大学) Modeling the evolution of topics with time is of great value in automatic summarization and analysis of large document collections. In this work, we p...
热度：34
140Online MKL for Structured Prediction[结构化预测的在线多核学习]
André F. T. Martins(卡内基梅隆大学) Structured prediction (SP) problems are characterized by strong interdependence among the output variables, usually with sequential, graphical, or com...
热度：43
André F. T. Martins(卡内基梅隆大学) Structured prediction (SP) problems are characterized by strong interdependence among the output variables, usually with sequential, graphical, or com...
热度：43