图 书 馆开课单位--丹麦技术大学
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1Large-scale Bayesian Inference for Collaborative Filtering[用于协同过滤的大规模贝叶斯推理]
Ole Winther(丹麦技术大学) The Netflix prize problem provides an excellent testing ground for machine learning. The problem is large scale and the data complex and noisy. It is ...
热度：39
Ole Winther(丹麦技术大学) The Netflix prize problem provides an excellent testing ground for machine learning. The problem is large scale and the data complex and noisy. It is ...
热度：39
2Well-known shortcomings, advantages and computational challenges in Bayesian modelling: a few case stories[众所周知的缺点，优点和贝叶斯模型计算的挑战：几个案例]
Ole Winther(丹麦技术大学) Bayesian inference can be used to judge the data fit quantitatively through the marginal likelihood. In many practical cases only one model is conside...
热度：40
Ole Winther(丹麦技术大学) Bayesian inference can be used to judge the data fit quantitatively through the marginal likelihood. In many practical cases only one model is conside...
热度：40
3Resampling Based Methods for Design and Evaluation of Neurotechnology[基于重采样的方法与评价技术设计]
Lars-Kai Hansen(丹麦技术大学) Brain imaging by PET, MR, EEG, and MEG has become a cornerstone in systems level neuroscience. Statistical analyses of neuroimage datasets face many i...
热度：48
Lars-Kai Hansen(丹麦技术大学) Brain imaging by PET, MR, EEG, and MEG has become a cornerstone in systems level neuroscience. Statistical analyses of neuroimage datasets face many i...
热度：48
4Modeling fMRI Dynamics[fMRI动力学建模]
Lars Kai Hansen(丹麦技术大学) Functional MRI modeling is challenged by long-range coupling, non-linearity, and lack of detailed physiological information. I will review our progres...
热度：36
Lars Kai Hansen(丹麦技术大学) Functional MRI modeling is challenged by long-range coupling, non-linearity, and lack of detailed physiological information. I will review our progres...
热度：36
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