图 书 馆开课单位--密歇根大学
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61Seeking and Offering Expertise across Categories: A Sustainable Mechanism Works for Baidu Knows[跨类别寻求和提供专业知识：百度知道的可持续机制
Xiao Wei(密歇根大学) This talk presents the first comprehensive exploration of the largest Chinese online knowledge sharing community- Baidu Knows. With analyzing 5.2 mill...
热度：53
Xiao Wei(密歇根大学) This talk presents the first comprehensive exploration of the largest Chinese online knowledge sharing community- Baidu Knows. With analyzing 5.2 mill...
热度：53
62Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations[用于分层表示的可扩展无监督学习的卷积深度信念网络]
Honglak Lee(密歇根大学) There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-size...
热度：54
Honglak Lee(密歇根大学) There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-size...
热度：54
63Training SVM with Indefinite Kernels[使用无限内核训练SVM]
Jieping Ye(密歇根大学) Similarity matrices generated from many applications may not be positive semidefinite, and hence can't fit into the kernel machine framework. In this ...
热度：62
Jieping Ye(密歇根大学) Similarity matrices generated from many applications may not be positive semidefinite, and hence can't fit into the kernel machine framework. In this ...
热度：62
64Efficiently Learning Linear-Linear Exponential Family Prof Stateedictive Representations[有效学习状态的线性线性指数族预测表示]
Satinder Singh(密歇根大学) Exponential Family PSR (EFPSR) models capture stochastic dynamical systems by representing state as the parameters of an exponential family distributi...
热度：81
Satinder Singh(密歇根大学) Exponential Family PSR (EFPSR) models capture stochastic dynamical systems by representing state as the parameters of an exponential family distributi...
热度：81
65Information Geometry: Duality, Convexity and Divergences[信息几何：二元性，凸性和分歧]
Jun Zhang(密歇根大学) In this talk, I explore the mathematical relationships between duality in information geometry, convex analysis, and divergence functions. First, fro...
热度：127
Jun Zhang(密歇根大学) In this talk, I explore the mathematical relationships between duality in information geometry, convex analysis, and divergence functions. First, fro...
热度：127
66Unifying Guilt-by-Association Approaches: Theorems and Fast Algorithms[用关联方法统一内疚：定理和快速算法]
Danai Koutra(密歇根大学) If several friends of Smith have committed petty thefts, what would you say about Smith? Most people would not be surprised if Smith is a hardened cri...
热度：85
Danai Koutra(密歇根大学) If several friends of Smith have committed petty thefts, what would you say about Smith? Most people would not be surprised if Smith is a hardened cri...
热度：85
67A Unified Framework for Multi-Target Tracking and Collective Activity Recognition[多目标跟踪和集体活动识别的统一框架]
Wongun Choi, Ivan Laptev, Michael J. Black(密歇根大学) We present a coherent, discriminative framework for simultaneously tracking multiple people and estimating their collective activities. Instead of tre...
热度：84
Wongun Choi, Ivan Laptev, Michael J. Black(密歇根大学) We present a coherent, discriminative framework for simultaneously tracking multiple people and estimating their collective activities. Instead of tre...
热度：84
68Academic Employment Networks and Departmental Rank[学术声望的就业网络和部门排名]
Debra Hevenstone(密歇根大学) Academic prestige rankings are stagnant over time despite departmental changes. Departments might maintain their prestige through hiring and training ...
热度：42
Debra Hevenstone(密歇根大学) Academic prestige rankings are stagnant over time despite departmental changes. Departments might maintain their prestige through hiring and training ...
热度：42
69Passive Learning with Target Risk[目标风险的被动学习]
Mehrdad Mahdavi(密歇根大学) In this paper we consider learning in passive setting but with a slight modification. We assume that the target expected loss, also referred to as tar...
热度：85
Mehrdad Mahdavi(密歇根大学) In this paper we consider learning in passive setting but with a slight modification. We assume that the target expected loss, also referred to as tar...
热度：85
70Consistency of Robust Kernel Density Estimators[鲁棒核密度估计的一致性]
Robert A. Vandermeulen(密歇根大学) The kernel density estimator (KDE) based on a radial positive-semidefinite kernel may be viewed as a sample mean in a reproducing kernel Hilbert space...
热度：120
Robert A. Vandermeulen(密歇根大学) The kernel density estimator (KDE) based on a radial positive-semidefinite kernel may be viewed as a sample mean in a reproducing kernel Hilbert space...
热度：120