图 书 馆开课单位--加州大学伯克利分校
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41Human memory search as a random walk in a semantic network[人类记忆搜索随机游动在语义网络中]
Abbott Joshua T(加州大学伯克利分校) The human mind has a remarkable ability to store a vast amount of information in memory, and an even more remarkable ability to retrieve these experie...
热度：48
Abbott Joshua T(加州大学伯克利分校) The human mind has a remarkable ability to store a vast amount of information in memory, and an even more remarkable ability to retrieve these experie...
热度：48
42Precisiation of meaning - A key to semantic computing[精准的意思——语义计算的关键]
Lotfi A. Zadeh(加州大学伯克利分校) 精准的意思——语义计算的关键
热度：15
Lotfi A. Zadeh(加州大学伯克利分校) 精准的意思——语义计算的关键
热度：15
43Pascal Challenge TU Darmstadt[帕斯卡挑战达姆施塔特]
Mario Fritz(加州大学伯克利分校) author: Mario Fritz, UC Berkeley published: Feb. 25, 2007, recorded: April 2005, views: 125 Categories Top » Computer Sc...
热度：20
Mario Fritz(加州大学伯克利分校) author: Mario Fritz, UC Berkeley published: Feb. 25, 2007, recorded: April 2005, views: 125 Categories Top » Computer Sc...
热度：20
44Gains in Power from Structured Two-Sample Test on Means of Graphs[从图的平均值的结构化双样本检验中获得效益]
Laurent Jacob(加州大学伯克利分校) Gains in Power from Structured Two-Sample Test on Means of Graphs
热度：37
Laurent Jacob(加州大学伯克利分校) Gains in Power from Structured Two-Sample Test on Means of Graphs
热度：37
45L1-based relaxations for sparsity recovery and graphical model selection in the high-dimensional regime[基于L1的松弛用于稀疏度恢复和高维体系中的图形模型选择]
Martin J. Wainwright(加州大学伯克利分校) The problem of estimating a sparse signal embedded in noise arises in various contexts, including signal denoising and approximation, as well as graph...
热度：78
Martin J. Wainwright(加州大学伯克利分校) The problem of estimating a sparse signal embedded in noise arises in various contexts, including signal denoising and approximation, as well as graph...
热度：78
46A Collaborative Mechanism for Crowdsourcing Prediction Problems[众包预测问题的协作机制]
Jacob Abernethy(加州大学伯克利分校) Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of “crowdsourcing” prediction tasks....
热度：28
Jacob Abernethy(加州大学伯克利分校) Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of “crowdsourcing” prediction tasks....
热度：28
47Statistical Inference of Protein Structure and Function[蛋白质结构与功能的统计推断]
Michael I. Jordan(加州大学伯克利分校) The study of the structure and function of proteins serves up many problems that offer challenges and opportunities for computational and statistical ...
热度：81
Michael I. Jordan(加州大学伯克利分校) The study of the structure and function of proteins serves up many problems that offer challenges and opportunities for computational and statistical ...
热度：81
48Distributed Dual Averaging In Networks[网络中的分布式双平均]
John Duchi(加州大学伯克利分校) The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly nonsmooth) convex functions...
热度：70
John Duchi(加州大学伯克利分校) The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly nonsmooth) convex functions...
热度：70
49Perceptual Bases for Rules of Thumb in Photography[摄影经验法则的知觉基础]
Martin S. Banks(加州大学伯克利分校) Photographers utilize many rules of thumb for creating natural-looking pictures. The explanations for these guidelines are vague and probably incorrec...
热度：35
Martin S. Banks(加州大学伯克利分校) Photographers utilize many rules of thumb for creating natural-looking pictures. The explanations for these guidelines are vague and probably incorrec...
热度：35
50An Additive Latent Feature Model for Transparent Object Recognition[透明目标识别的加性潜在特征模型]
Mario Fritz(加州大学伯克利分校) Existing methods for recognition of object instances and categories based on quantized local features can perform poorly when local features exist on ...
热度：46
Mario Fritz(加州大学伯克利分校) Existing methods for recognition of object instances and categories based on quantized local features can perform poorly when local features exist on ...
热度：46