图 书 馆开课单位--纽约大学
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41Inference and Learning with Networked Data[网络数据推理与学习]
Foster Provost(纽约大学) In many applications we would like to draw inferences about entities that are interconnected in complex networks. For example, calls, emails, IM, and...
热度：28
Foster Provost(纽约大学) In many applications we would like to draw inferences about entities that are interconnected in complex networks. For example, calls, emails, IM, and...
热度：28
42Probabilistic decision-making, data analysis, and discovery in astronomy[天文学中的概率决策、数据分析和发现]
David W. Hogg(纽约大学) Astronomy is a prime user community for machine learning and probabilistic modeling. There are very large, public data sets (mostly but not entirely ...
热度：29
David W. Hogg(纽约大学) Astronomy is a prime user community for machine learning and probabilistic modeling. There are very large, public data sets (mostly but not entirely ...
热度：29
43Representation of Value in the Primate Brain[在灵长类动物脑中的代表价值]
Paul W. Glimcher(纽约大学) Pigeons really like millet seed, monkeys crave juice, and humans get a kick out of winning money. While all animals don’t enjoy the same rewards...
热度：42
Paul W. Glimcher(纽约大学) Pigeons really like millet seed, monkeys crave juice, and humans get a kick out of winning money. While all animals don’t enjoy the same rewards...
热度：42
44Hierarchical spike coding of sound[声音的分级峰值编码]
Yan Karklin(纽约大学) We develop a probabilistic generative model for representing acoustic event structure at multiple scales via a two-stage hierarchy. The first stage co...
热度：70
Yan Karklin(纽约大学) We develop a probabilistic generative model for representing acoustic event structure at multiple scales via a two-stage hierarchy. The first stage co...
热度：70
45Content and Causality in Influence Networks[影响网络的内容与因果关系]
Sinan Aral(纽约大学) Many of us are interested in whether "networks matter." Whether in the spread of disease, the diffusion of information, the propagation of b...
热度：30
Sinan Aral(纽约大学) Many of us are interested in whether "networks matter." Whether in the spread of disease, the diffusion of information, the propagation of b...
热度：30
46Stability of Transductive Regression Algorithms[转导回归算法的稳定性]
Ashish Rastogi(纽约大学) This paper uses the notion of algorithmic stability to derive novel generalization bounds for several families of transductive regression algorithms, ...
热度：74
Ashish Rastogi(纽约大学) This paper uses the notion of algorithmic stability to derive novel generalization bounds for several families of transductive regression algorithms, ...
热度：74
47Dynamic Factor Graphs for Time Series Modeling[时间序列建模的动态因子图]
Piotr Mirowski(纽约大学) This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint ...
热度：132
Piotr Mirowski(纽约大学) This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint ...
热度：132
48Indoor Segmentation and Support Inference from RGBD Images[基于RGBD图像推断支持实现室内对象分割]
Silvio Savarese, Nathan Silberman, Aude Oliva(纽约大学) We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignor...
热度：160
Silvio Savarese, Nathan Silberman, Aude Oliva(纽约大学) We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignor...
热度：160