图 书 馆开课单位--柏林大学
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1The Gaussian Variational Approximation of Stochastic Differential Equations[随机微分方程的高斯变分逼近]
Manfred Opper(柏林大学) The Gaussian Variational Approximation of Stochastic Differential Equations
热度：16
Manfred Opper(柏林大学) The Gaussian Variational Approximation of Stochastic Differential Equations
热度：16
2Probabilistic inference methods in robotics-filling the gap between high-level reasoning and low-level motion control[机器人中的概率推理方法填补了高级推理和低级运动控制之间的空白]
Marc Toussaint(柏林大学) Probabilistic inference methods in robotics-filling the gap between high-level reasoning and low-level motion control
热度：24
Marc Toussaint(柏林大学) Probabilistic inference methods in robotics-filling the gap between high-level reasoning and low-level motion control
热度：24
3Analysing Non-stationarity in EEG-BCI[EEG-BCI的非平稳性分析]
Klaus-Robert Müller(柏林大学) EEG is a highly complex signal. One of the main challenges of EEG analysis is to robustify against artifacts, non-stationarities and task unrelated va...
热度：102
Klaus-Robert Müller(柏林大学) EEG is a highly complex signal. One of the main challenges of EEG analysis is to robustify against artifacts, non-stationarities and task unrelated va...
热度：102
4Novel Computational and Recording Techniques for Studying Neuronal Oscillations Acquired with EEG/MEG[研究EEG/MEG获得的神经元振荡的新计算和记录技术]
Vadim Nikulin(柏林大学) In the first part of the talk I will present a new type of EEG electrodes. Current mainstream EEG electrode setups in BCI research permit efficient re...
热度：51
Vadim Nikulin(柏林大学) In the first part of the talk I will present a new type of EEG electrodes. Current mainstream EEG electrode setups in BCI research permit efficient re...
热度：51
5Novel Computational and Recording Techniques for Studying Neuronal Oscillations Acquired with EEG/MEG[用脑电/脑电地形图研究神经元振荡的新计算和记录技术]
Vadim Nikulin(柏林大学) In the first part of the talk I will present a new type of EEG electrodes. Current mainstream EEG electrode setups in BCI research permit efficient re...
热度：73
Vadim Nikulin(柏林大学) In the first part of the talk I will present a new type of EEG electrodes. Current mainstream EEG electrode setups in BCI research permit efficient re...
热度：73
6Gentle Introduction to Signal Processing and Classification for Single-Trial ERP Analysis[浅谈单次ERP分析中的信号处理与分类]
Benjamin Blankertz(柏林大学) The aim of this lecture is to provide an illustrative tutorial on the methods for single-trial ERP analysis. Basic concepts of feature extraction and ...
热度：156
Benjamin Blankertz(柏林大学) The aim of this lecture is to provide an illustrative tutorial on the methods for single-trial ERP analysis. Basic concepts of feature extraction and ...
热度：156
7Regularized Sparse Kernel Slow Feature Analysis[正则化稀疏核慢特征分析]
Wendelin Böhmer(柏林大学) This paper develops a kernelized slow feature analysis (SFA) algorithm. SFA is an unsupervised learning method to extract features which encode latent...
热度：75
Wendelin Böhmer(柏林大学) This paper develops a kernelized slow feature analysis (SFA) algorithm. SFA is an unsupervised learning method to extract features which encode latent...
热度：75
8Novel Computational and Recording Techniques for Studying Neuronal Oscillations Acquired with EEG/MEG[研究脑电图/脑磁图神经振荡的新计算和记录技术]
Vadim Nikulin(柏林大学) In the first part of the talk I will present a new type of EEG electrodes. Current mainstream EEG electrode setups in BCI research permit efficient re...
热度：76
Vadim Nikulin(柏林大学) In the first part of the talk I will present a new type of EEG electrodes. Current mainstream EEG electrode setups in BCI research permit efficient re...
热度：76
9The Sparse Grid Method[稀疏网格法]
Jochen Garcke(柏林大学) The sparse grid method is a special discretization technique, which allows to cope with the curse of dimensionality to some extent. It is based on a h...
热度：723
Jochen Garcke(柏林大学) The sparse grid method is a special discretization technique, which allows to cope with the curse of dimensionality to some extent. It is based on a h...
热度：723
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