图 书 馆开课单位--华盛顿大学
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61Efficient Regression for Computational Photography: from Color Management to Omnidirectional Superresolution[计算摄影的有效回归：从色彩管理到全方位超分辨率 ]
Maya Gupta(华盛顿大学) Many computational photography applications can be framed as low-dimensional regression problems that require fast evaluation of test samples for rend...
热度：28
Maya Gupta(华盛顿大学) Many computational photography applications can be framed as low-dimensional regression problems that require fast evaluation of test samples for rend...
热度：28
62Linear Bellman Equations: Theory and Applications[线性bellman方程：理论与应用 ]
Emanuel Todorov(华盛顿大学) I will provide a brief overview of a class stochastic optimal control problems recently developed by our group as well as by Bert Kappen's group. This...
热度：52
Emanuel Todorov(华盛顿大学) I will provide a brief overview of a class stochastic optimal control problems recently developed by our group as well as by Bert Kappen's group. This...
热度：52
63Linear Bellman Combination for Simulation of Human Motion[模拟人体运动的线性bellman组合方法 ]
Jovan Popović(华盛顿大学) Simulation of natural human motion is challenging because the relevant system dynamics is high-dimensional, underactuated—no direct control over...
热度：43
Jovan Popović(华盛顿大学) Simulation of natural human motion is challenging because the relevant system dynamics is high-dimensional, underactuated—no direct control over...
热度：43
64Large-Scale Learning and Inference: What We Have Learned with Markov Logic Networks[大规模学习和推理：我们用马尔可夫逻辑网络学到的东西]
Pedro Domingos(华盛顿大学 ) Markov logic allows very large and rich graphical models to be compactly specified. Current learning and inference algorithms for Markov logic can rou...
热度：58
Pedro Domingos(华盛顿大学 ) Markov logic allows very large and rich graphical models to be compactly specified. Current learning and inference algorithms for Markov logic can rou...
热度：58
65Discriminative Learning of Sum-Product Networks[和积网络的判别学习]
Robert Gens(华盛顿大学) Sum-product networks are a new deep architecture that can perform fast, exact inference on high-treewidth models. Only generative methods for training...
热度：44
Robert Gens(华盛顿大学) Sum-product networks are a new deep architecture that can perform fast, exact inference on high-treewidth models. Only generative methods for training...
热度：44
66Latent Factor Models for Relational Arrays and Network Data[关系阵列和网络数据的潜在因素模型]
Peter Hoff(华盛顿大学) Network and relational data structures have increasingly played a role in the understanding of complex biological, social and other relational systems...
热度：57
Peter Hoff(华盛顿大学) Network and relational data structures have increasingly played a role in the understanding of complex biological, social and other relational systems...
热度：57
67Kernel Descriptors for Visual Recognition[视觉识别的核心描述符]
Liefeng Bo(华盛顿大学) The design of low-level image features is critical for computer vision algorithms. Orientation histograms, such as those in SIFT~\cite{Lowe2004Distinc...
热度：53
Liefeng Bo(华盛顿大学) The design of low-level image features is critical for computer vision algorithms. Orientation histograms, such as those in SIFT~\cite{Lowe2004Distinc...
热度：53
68Sharing Features among Dynamical Systems with Beta Processes[与beta过程共享动态系统的特征]
Emily Fox(华盛顿大学) We propose a Bayesian nonparametric approach to relating multiple time series via a set of latent, dynamical behaviors. Using a beta process prior, we...
热度：38
Emily Fox(华盛顿大学) We propose a Bayesian nonparametric approach to relating multiple time series via a set of latent, dynamical behaviors. Using a beta process prior, we...
热度：38
69Similarity-Based Classifiers: Problems and Solutions [基于相似度的分类器：问题和解决方案]
Maya Gupta(华盛顿大学) Similarity-based learning assumes one is given similarities between samples to learn from, and can be considered a special case of graph-based learnin...
热度：94
Maya Gupta(华盛顿大学) Similarity-based learning assumes one is given similarities between samples to learn from, and can be considered a special case of graph-based learnin...
热度：94
70The stability of a good clustering[稳定的聚类很好]
Marina Meila(华盛顿大学) If we have found a "good" clustering C of data set X, can we prove that C is not far from the (unknown) best clustering C* of this data set? Perhaps s...
热度：49
Marina Meila(华盛顿大学) If we have found a "good" clustering C of data set X, can we prove that C is not far from the (unknown) best clustering C* of this data set? Perhaps s...
热度：49