图 书 馆开课单位--华盛顿大学
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1112Creating Infinitely Adaptable Courseware[创建无限适应性课件]
Zoran Popović(华盛顿大学) What does it take to create an infinitely adaptable courseware that creates optimal learning pathways for each student? Such courseware needs to have ...
热度：33
Zoran Popović(华盛顿大学) What does it take to create an infinitely adaptable courseware that creates optimal learning pathways for each student? Such courseware needs to have ...
热度：33
13On combining graph-based variance reduction schemes[基于组合图的方差缩减方案研究]
Vibhav Gogate(华盛顿大学) In this paper, we consider two variance reduction schemes that exploit the structure of the primal graph of the graphical model: Rao-Blackwellised w-c...
热度：38
Vibhav Gogate(华盛顿大学) In this paper, we consider two variance reduction schemes that exploit the structure of the primal graph of the graphical model: Rao-Blackwellised w-c...
热度：38
14Is Deep Learning the New 42?[深度学习是新的42吗？]
Pedro Domingos(华盛顿大学) The history of deep learning goes back more than five decades but in the marketplace of ideas its perceived value went through booms and busts. We are...
热度：37
Pedro Domingos(华盛顿大学) The history of deep learning goes back more than five decades but in the marketplace of ideas its perceived value went through booms and busts. We are...
热度：37
15Fast Flux Discriminant for Large-Scale Sparse Nonlinear Classification[大规模稀疏非线性分类的快速流量判别法]
Wenlin Chen(华盛顿大学) In this paper, we propose a novel supervised learning method, Fast Flux Discriminant (FFD), for large-scale nonlinear classification. Compared with ot...
热度：42
Wenlin Chen(华盛顿大学) In this paper, we propose a novel supervised learning method, Fast Flux Discriminant (FFD), for large-scale nonlinear classification. Compared with ot...
热度：42
16A Naturalistic Open Source Movie for Optical Flow Evaluation[用于光流评估的自然开源电影]
Daniel J. Butler(华盛顿大学) Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of s...
热度：61
Daniel J. Butler(华盛顿大学) Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of s...
热度：61
17Machine Learning for the Web: A Unified View[Web机器学习：统一视图]
Pedro Domingos(华盛顿大学) Machine learning and the Web are a technology and an application area made for each other. The Web provides machine learning with an ever-growing stre...
热度：47
Pedro Domingos(华盛顿大学) Machine learning and the Web are a technology and an application area made for each other. The Web provides machine learning with an ever-growing stre...
热度：47
18Information Arbitrage Across Multi-lingual Wikipedia[跨语言维基百科的信息套利]
Daniel S. Weld;Michael Skinner;Eytan Adar(华盛顿大学) Information Arbitrage Across Multi-lingual Wikipedia
热度：45
Daniel S. Weld;Michael Skinner;Eytan Adar(华盛顿大学) Information Arbitrage Across Multi-lingual Wikipedia
热度：45
19Collaboration Over Time: Characterizing and Modeling Network Evolution.[随着时间的推移协作：网络演化的特征和建模。 ]
Jian Huang(华盛顿大学) Collaboration Over Time: Characterizing and Modeling Network Evolution.
热度：59
Jian Huang(华盛顿大学) Collaboration Over Time: Characterizing and Modeling Network Evolution.
热度：59
20Multi-way Gaussian Graphical Models with Application to Multivariate Lattice Data[多元高斯图模型及其在多元格子数据中的应用]
Adrian Dobra(华盛顿大学) The literature on Gaussian graphical models (GGMs) contains two equally rich and equally significant domains of research efforts and interests. The fi...
热度：56
Adrian Dobra(华盛顿大学) The literature on Gaussian graphical models (GGMs) contains two equally rich and equally significant domains of research efforts and interests. The fi...
热度：56