图 书 馆开课单位--犹他大学
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1Coresets for Kernel Regression[核回归的核心集]
Yan Zheng(犹他大学) Kernel regression is an essential and ubiquitous tool for non-parametric data analysis, particularly popular among time series and spatial data. Howev...
热度：15
Yan Zheng(犹他大学) Kernel regression is an essential and ubiquitous tool for non-parametric data analysis, particularly popular among time series and spatial data. Howev...
热度：15
2Compass: Spatio Temporal Sentiment Analysis of US Election[指南针：美国大选的时空情绪分析]
Debjyoti Paul(犹他大学) With the widespread growth of various social network tools and platforms, analyzing and understanding societal response and crowd reaction to importan...
热度：20
Debjyoti Paul(犹他大学) With the widespread growth of various social network tools and platforms, analyzing and understanding societal response and crowd reaction to importan...
热度：20
3Controlling the Risk of Conversational Search via Reinforcement Learning[通过强化学习控制会话搜索的风险]
Zhenduo Wang(犹他大学) Controlling the Risk of Conversational Search via Reinforcement Learning
热度：24
Zhenduo Wang(犹他大学) Controlling the Risk of Conversational Search via Reinforcement Learning
热度：24
4Maximizing Marginal Fairness for Dynamic Learning to Rank[动态学习排名的边际公平性最大化]
Tao Yang(犹他大学) Maximizing Marginal Fairness for Dynamic Learning to Rank
热度：31
Tao Yang(犹他大学) Maximizing Marginal Fairness for Dynamic Learning to Rank
热度：31
5Maximizing Marginal Fairness for Dynamic Learning to Rank[最大化动态学习排名的边际公平性]
Tao Yang(犹他大学) Maximizing Marginal Fairness for Dynamic Learning to Rank
热度：49
Tao Yang(犹他大学) Maximizing Marginal Fairness for Dynamic Learning to Rank
热度：49
6Co-regularized Spectral Clustering with Multiple Kernels[多核谱聚类算法的有限正规化]
Piyush Rai(犹他大学) We propose a co-regularization based multiview spectral clustering algorithm which enforces the clusterings across multiple views to agree with each-o...
热度：67
Piyush Rai(犹他大学) We propose a co-regularization based multiview spectral clustering algorithm which enforces the clusterings across multiple views to agree with each-o...
热度：67
7Multitask Learning Using Nonparametrically Learned Predictor Subspaces[基于非参数学习预测子空间的多任务学习 ]
Piyush Rai(犹他大学) Given several related learning tasks, we propose a nonparametric Bayesian learning model that captures task relatedness by assuming that the task para...
热度：89
Piyush Rai(犹他大学) Given several related learning tasks, we propose a nonparametric Bayesian learning model that captures task relatedness by assuming that the task para...
热度：89
8Locomotion Interface for Outdoor Virtual Environments[户外虚拟环境的运动接口]
John M. Hollerbach(犹他大学 ) Locomotion Interface for Outdoor Virtual Environments The Treadport Active Wind Tunnel (TPAWT pronounced teapot) is a treadmill-style locomotion inter...
热度：75
John M. Hollerbach(犹他大学 ) Locomotion Interface for Outdoor Virtual Environments The Treadport Active Wind Tunnel (TPAWT pronounced teapot) is a treadmill-style locomotion inter...
热度：75
9Universal Multi-Dimensional Scaling[通用多维标度]
Arvind Agarwal(犹他大学) In this paper, we propose a unified algorithmic framework for solving many known variants of MDS. Our algorithm is a simple iterative scheme with guar...
热度：56
Arvind Agarwal(犹他大学) In this paper, we propose a unified algorithmic framework for solving many known variants of MDS. Our algorithm is a simple iterative scheme with guar...
热度：56
10Polymodal Sensory Integration in the Visual System[视觉系统中的多模态感觉整合]
David Križaj(犹他大学) In many, perhaps most, vertebrate species, vision represents a dominant sensory modality that is essential for orientation and communication with the ...
热度：95
David Križaj(犹他大学) In many, perhaps most, vertebrate species, vision represents a dominant sensory modality that is essential for orientation and communication with the ...
热度：95