开课单位--加州大学伯克利分校
41
Human 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
42
Precisiation 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
43
Pascal 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
44
Gains 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
45
L1-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
46
A 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
47
Statistical 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
48
Distributed 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
49
Perceptual 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
50
An 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