开课单位--哥伦比亚大学
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21
Distilled Sensing: Active sensing for sparse recovery[蒸馏感测:主动感测,稀疏恢复]
  Rui Castro(哥伦比亚大学) The study and use of sparse representations in data-rich applications has garnered signi cant attention in the signal processing, statistics, and mach...
热度:38

22
Multi-Task Discriminative Estimation for Generative Models and Probabilities[生成模型和概率的多任务判别估计]
  Tony Jebara(哥伦比亚大学) Maximum entropy discrimination is a method for estimating distributions such that they meet classification constraints and perform accurate prediction...
热度:67

23
Information Rates and Optimal Decoding in Large Neural Populations[大神经群中的信息率与最优译码]
  David Pfau(哥伦比亚大学) Many fundamental questions in theoretical neuroscience involve optimal decoding and the computation of Shannon information rates in populations of spi...
热度:43

24
Identifying Dendritic Processing[鉴别树枝状加工]
  Aurel A. Lazar(哥伦比亚大学) In system identification both the input and the output of a system are available to an observer and an algorithm is sought to identify parameters of a...
热度:47

25
MAP Estimation with Perfect Graphs[完美图的MAP估计]
  Tony Jebara(哥伦比亚大学) Efficiently finding the maximum a posteriori (MAP) configuration of a graphical model is an important problem which is often implemented using message...
热度:65

26
Dynamic Bayesian Networks for Multimodal Interaction[用于多模式交互的动态贝叶斯网络]
  Tony Jebara(哥伦比亚大学) Dynamic Bayesian networks (DBNs) offer a natural upgrade path beyond classical hidden Markov models and become especially relevant when temporal data ...
热度:57

27
The Story of American Freedom: 1776-2005[美国自由的故事:1776-2005]
  Eric Foner(哥伦比亚大学) Although the idea of freedom is nearly ubiquitous in American public discourse -- and perhaps no more so than today – it has been subject to a r...
热度:88

28
Majorization for CRFs and Latent Likelihoods[CRF的专业化和潜在的可能性]
  Anna Choromanska(哥伦比亚大学) The partition function plays a key role in probabilistic modeling including conditional random fields, graphical models, and maximum likelihood estima...
热度:52

29
Conformal Multi-Instance Kernels[正交多实例内核]
  Matthew B. Blaschko(哥伦比亚大学) In the multiple instance learning setting, each observation is a bag of feature vectors of which one or more vectors indicates membership in a class....
热度:66

30
Learning a Distance Metric for Structured Network Prediction[结构化网络预测的距离度量学习 ]
  Stuart Andrews(哥伦比亚大学) Man-made or naturally-formed networks typically exhibit a high degree of structural regularity. In this paper, we introduce the problem of structured...
热度:98
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