We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.
We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.
Journal Article - page 2
Robust optimization in the presence of uncertainty: A generic approach
We propose a novel approach for optimization under uncertainty. Our approach does not assume any particular noise model behind the measurements, and only requires two typical instances. We first propose a measure of similarity of instances (with respect to a given objective). Based on this measure, we then choose a solution randomly among all solutions that are near-optimum for both instances. The exact notion of near-optimum is intertwined with the proposed similarity measure. Our similarity measure also allows us to...
Posterior agreement for large parameter-rich optimization problems
Automatic Human Sleep Stage Scoring Using Deep Neural Networks
The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods...
A generative model of whole-brain effective connectivity
The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it scales gracefully to very large networks. However, large-scale networks with thousands of connections are difficult to interpret; additionally, one typically lacks information (data points per free parameter) for precise estimation of all model parameters. This paper introduces sparsity constraints...