开课单位--马克斯普朗克研究所

71
Learning from Incomplete Data with Infinite Imputations[从与无限之间的不完全数据中学习]
  Uwe Dick(马克斯普朗克研究所) We address the problem of learning decision functions from training data in which some attribute values are unobserved. This problem can arise for ins...
热度：42

72
Distinguishing Causes from Effects using Nonlinear Acyclic Causal Models[使用非线性非循环因果模型的影响的区分原因]
  Kun Zhang;Patrik Hoyer; Aapo Hyvärinen(马克斯普朗克研究所) 使用非线性非循环因果模型的影响的区分原因
热度：39

73
Kernel Tricks, Means and Ends[核心技巧，手段和目的]
  Bernhard Schölkopf(马克斯普朗克研究所) I will present my thoughts on what made kernel machines popular and what may or may not keep them going. I will also discuss applications in different...
热度：36

74
Generalized Dictionary Learning for Symmetric Positive Definite Matrices with Application to Nearest Neighbor Retrieval[对称正定矩阵的广义字典学习及其在最近邻检索中的应用]
  Suvrit Sra(马克斯普朗克研究所) We introduce Generalized Dictionary Learning (GDL), a simple but practical framework for learning dictionaries over the manifold of positive definite ...
热度：51

75
Timely Knowledge[及时的知识]
  Gerhard Weikum(马克斯普朗克研究所) 及时的知识 
热度：27

76
Getting lost in space: Large sample analysis of the resistance distance[空间损耗：电阻距离的大样本分析]
  Matthias Hein(马克斯普朗克研究所) The commute distance between two vertices in a graph is the expected time it takes a random walk to travel from the first to the second vertex and bac...
热度：21

77
How to choose the covariance for Gaussian process regression independently of the basis[如何独立选择高斯过程回归协方差的基础]
  Matthias O. Franz(马克斯普朗克研究所) In Gaussian process regression, both the basis functions and their prior distribution are simultaneously specified by the choice of the covariance fun...
热度：14

78
Interpreting Covariance Functions & Classification[协方差函数的解释与分类]
  Carl Edward Rasmussen(马克斯普朗克研究所) 协方差函数的解释与分类
热度：54

79
Real-time Population of Knowledge Bases: Opportunities and Challenges[知识基础的实时人口：机遇与挑战]
  Ndapandula Nakashole(马克斯普朗克研究所) Dynamic content is a frequently accessed part of the Web. However, most information extraction approaches are batch-oriented, thus not effective for g...
热度：15

80
Bayesian inference and Gaussian processes[贝叶斯推理和高斯过程]
  Carl Edward Rasmussen(马克斯普朗克研究所) Top » Computer Science » Machine Learning » Bayesian Learning Top » Computer Science » Machine Learning » Gaussia...
热度：306