图 书 馆开课单位--杜克大学
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21Augment-and-Conquer Negative Binomial Processes[增强和克服负二项过程]
Mingyuan Zhou(杜克大学) By developing data augmentation methods unique to the negative binomial (NB) distribution, we unite seemingly disjoint count and mixture models under ...
热度：62
Mingyuan Zhou(杜克大学) By developing data augmentation methods unique to the negative binomial (NB) distribution, we unite seemingly disjoint count and mixture models under ...
热度：62
22Bayesian dynamic modelling[贝叶斯动态建模 ]
Michael I. Jordan, Mike West(杜克大学) Since the 1970s, applications of Bayesian time series models and forecasting methods have represented major success stories for our discipline. Dynami...
热度：104
Michael I. Jordan, Mike West(杜克大学) Since the 1970s, applications of Bayesian time series models and forecasting methods have represented major success stories for our discipline. Dynami...
热度：104
23Nonparametric Factor Analysis with Beta Process Priors[贝塔过程先验的非参数因子分析]
John Paisley(杜克大学) We propose a nonparametric extension to the factor analysis problem using a beta process prior. This beta process factor analysis (BPFA) model allows ...
热度：245
John Paisley(杜克大学) We propose a nonparametric extension to the factor analysis problem using a beta process prior. This beta process factor analysis (BPFA) model allows ...
热度：245
24An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning[强化学习的线性模型，线性值 - 函数逼近和特征选择分析]
Ronald Parr(杜克大学 ) We show that linear value function approximation is equivalent to a form of linear model approximation. We derive a relationship between the model app...
热度：102
Ronald Parr(杜克大学 ) We show that linear value function approximation is equivalent to a form of linear model approximation. We derive a relationship between the model app...
热度：102
25Autonomous Geometric Precision Error Estimation in Low-level Computer Vision Tasks[低级计算机视觉任务中的自主几何精度误差估计]
John Paisley(杜克大学) Errors in map-making tasks using computer vision are sparse. We demonstrate this by considering the construction of digital elevation models that empl...
热度：76
John Paisley(杜克大学) Errors in map-making tasks using computer vision are sparse. We demonstrate this by considering the construction of digital elevation models that empl...
热度：76
26Multi-Task Compressive Sensing with Dirichlet Process Priors[基于Dirichlet过程先验的多任务压缩感知]
Lawrence Carin(杜克大学) Compressive sensing (CS) is an emerging field that, under appropriate conditions, can significantly reduce the number of measurements required for a g...
热度：95
Lawrence Carin(杜克大学) Compressive sensing (CS) is an emerging field that, under appropriate conditions, can significantly reduce the number of measurements required for a g...
热度：95
27Hierarchical Kernel Stick-Breaking Process for Multi-Task Image Analysis[多任务图像分析的分层核破坏过程]
Lawrence Carin(杜克大学) The kernel stick-breaking process (KSBP) is employed to segment general imagery, imposing the condition that patches (small blocks of pixels) that are...
热度：73
Lawrence Carin(杜克大学) The kernel stick-breaking process (KSBP) is employed to segment general imagery, imposing the condition that patches (small blocks of pixels) that are...
热度：73
28Estimation of Extreme Values and Associated Level Sets of a Regression Function via Selective Sampling[通过选择性采样估计回归函数的极值和相关水平集]
Stanislav Minsker(杜克大学) We propose a new method for estimating the locations and the value of an absolute maximum (minimum) of a function from the observations contaminated b...
热度：77
Stanislav Minsker(杜克大学) We propose a new method for estimating the locations and the value of an absolute maximum (minimum) of a function from the observations contaminated b...
热度：77
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