Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

28 results (View BibTeX file of all listed publications)

2017

Nonparametric Disturbance Correction and Nonlinear Dual Control

(24098), ETH Zurich, 2017 (phdthesis)

2017

Klenske, E. D. Nonparametric Disturbance Correction and Nonlinear Dual Control (24098), ETH Zurich, 2017 (phdthesis)

DOI [BibTex]

Development and Evaluation of a Portable BCI System for Remote Data Acquisition

Emde, T.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

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Emde, T. Development and Evaluation of a Portable BCI System for Remote Data Acquisition Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

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Brain-Computer Interfaces for patients with Amyotrophic Lateral Sclerosis

Fomina, T.

Eberhard Karls Universität Tübingen, Germany, 2017 (phdthesis)

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Fomina, T. Brain-Computer Interfaces for patients with Amyotrophic Lateral Sclerosis Eberhard Karls Universität Tübingen, Germany, 2017 (phdthesis)

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Causal models for decision making via integrative inference

Geiger, P.

University of Stuttgart, Germany, 2017 (phdthesis)

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Geiger, P. Causal models for decision making via integrative inference University of Stuttgart, Germany, 2017 (phdthesis)

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Learning Optimal Configurations for Modeling Frowning by Transcranial Electrical Stimulation

Sücker, K.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

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Sücker, K. Learning Optimal Configurations for Modeling Frowning by Transcranial Electrical Stimulation Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

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2015

easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies

Grimm, Dominik

Eberhard Karls Universität Tübingen, November 2015 (phdthesis)

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2015

Grimm, Dominik easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies Eberhard Karls Universität Tübingen, November 2015 (phdthesis)

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Causal Discovery Beyond Conditional Independences

Sgouritsa, E.

Eberhard Karls Universität Tübingen, Germany, October 2015 (phdthesis)

link (url) [BibTex]

Sgouritsa, E. Causal Discovery Beyond Conditional Independences Eberhard Karls Universität Tübingen, Germany, October 2015 (phdthesis)

link (url) [BibTex]

From Points to Probability Measures: A Statistical Learning on Distributions with Kernel Mean Embedding

Muandet, K.

University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

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Muandet, K. From Points to Probability Measures: A Statistical Learning on Distributions with Kernel Mean Embedding University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

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Machine Learning Approaches to Image Deconvolution

Schuler, C.

University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

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Schuler, C. Machine Learning Approaches to Image Deconvolution University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

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Blind Retrospective Motion Correction of MR Images

Loktyushin, A.

University of Tübingen, Germany, May 2015 (phdthesis)

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Loktyushin, A. Blind Retrospective Motion Correction of MR Images University of Tübingen, Germany, May 2015 (phdthesis)

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A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Hohmann, M.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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Hohmann, M. A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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Sequential Image Deconvolution Using Probabilistic Linear Algebra

Gao, M.

Technical University of Munich, Germany, 2015 (mastersthesis)

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Gao, M. Sequential Image Deconvolution Using Probabilistic Linear Algebra Technical University of Munich, Germany, 2015 (mastersthesis)

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Causal Inference in Neuroimaging

Casarsa de Azevedo, L.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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Casarsa de Azevedo, L. Causal Inference in Neuroimaging Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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The effect of frowning on attention

Ibarra Chaoul, A.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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Ibarra Chaoul, A. The effect of frowning on attention Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

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2006

Extraction of visual features from natural video data using Slow Feature Analysis

Nickisch, H.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, September 2006 (diplomathesis)

Abstract

Das Forschungsprojekt NeuRoBot hat das un{\"u}berwachte Erlernen einer neuronal inspirierten Steuerungsarchitektur zum Ziel, und zwar unter den Randbedingungen biologischer Plausibilit{\"a}t und der Benutzung einer Kamera als einzigen Sensor. Visuelle Merkmale, die ein angemessenes Abbild der Umgebung liefern, sind unerl{\"a}sslich, um das Ziel kollisionsfreier Navigation zu erreichen. Zeitliche Koh{\"a}renz ist ein neues Lernprinzip, das in der Lage ist, Erkenntnisse aus der Biologie des Sehens zu reproduzieren. Es wird durch die Beobachtung motiviert, dass die Sensoren der Retina auf deutlich k{\"u}rzeren Zeitskalen variieren als eine abstrakte Beschreibung. Zeitliche Langsamkeitsanalyse l{\"o}st das Problem, indem sie zeitlich langsam ver{\"a}nderliche Signale aus schnell ver{\"a}nderlichen Eingabesignalen extrahiert. Eine Verallgemeinerung auf Signale, die nichtlinear von den Eingaben abh{\"a}ngen, ist durch die Anwendung des Kernel-Tricks m{\"o}glich. Das einzig benutzte Vorwissen ist die zeitliche Glattheit der gewonnenen Signale. In der vorliegenden Diplomarbeit wird Langsamkeitsanalyse auf Bildausschnitte von Videos einer Roboterkamera und einer Simulationsumgebung angewendet. Zuallererst werden mittels Parameterexploration und Kreuzvalidierung die langsamst m{\"o}glichen Funktionen bestimmt. Anschließend werden die Merkmalsfunktionen analysiert und einige Ansatzpunkte f{\"u}r ihre Interpretation angegeben. Aufgrund der sehr großen Datens{\"a}tze und der umfangreichen Berechnungen behandelt ein Großteil dieser Arbeit auch Aufwandsbetrachtungen und Fragen der effizienten Berechnung. Kantendetektoren in verschiedenen Phasen und mit haupts{\"a}chlich horizontaler Orientierung stellen die wichtigsten aus der Analyse hervorgehenden Funktionen dar. Eine Anwendung auf konkrete Navigationsaufgaben des Roboters konnte bisher nicht erreicht werden. Eine visuelle Interpretation der erlernten Merkmale ist jedoch durchaus gegeben.

PDF [BibTex]

2006

Nickisch, H. Extraction of visual features from natural video data using Slow Feature Analysis Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, September 2006 (diplomathesis)

PDF [BibTex]

An Online-Computation Approach to Optimal Finite-Horizon State-Feedback Control of Nonlinear Stochastic Systems

Deisenroth, MP.

Biologische Kybernetik, Universität Karlsruhe (TH), Karlsruhe, Germany, August 2006 (diplomathesis)

PDF [BibTex]

Deisenroth, MP. An Online-Computation Approach to Optimal Finite-Horizon State-Feedback Control of Nonlinear Stochastic Systems Biologische Kybernetik, Universität Karlsruhe (TH), Karlsruhe, Germany, August 2006 (diplomathesis)

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Object Classification using Local Image Features

Nowozin, S.

Biologische Kybernetik, Technical University of Berlin, Berlin, Germany, May 2006 (diplomathesis)

Abstract

Object classification in digital images remains one of the most challenging tasks in computer vision. Advances in the last decade have produced methods to repeatably extract and describe characteristic local features in natural images. In order to apply machine learning techniques in computer vision systems, a representation based on these features is needed. A set of local features is the most popular representation and often used in conjunction with Support Vector Machines for classification problems. In this work, we examine current approaches based on set representations and identify their shortcomings. To overcome these shortcomings, we argue for extending the set representation into a graph representation, encoding more relevant information. Attributes associated with the edges of the graph encode the geometric relationships between individual features by making use of the meta data of each feature, such as the position, scale, orientation and shape of the feature region. At the same time all invariances provided by the original feature extraction method are retained. To validate the novel approach, we use a standard subset of the ETH-80 classification benchmark.

PDF [BibTex]

Nowozin, S. Object Classification using Local Image Features Biologische Kybernetik, Technical University of Berlin, Berlin, Germany, May 2006 (diplomathesis)

PDF [BibTex]

Kernel PCA for Image Compression

Huhle, B.

Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, Germany, April 2006 (diplomathesis)

PDF [BibTex]

Huhle, B. Kernel PCA for Image Compression Biologische Kybernetik, Eberhard-Karls-Universität, Tübingen, Germany, April 2006 (diplomathesis)

PDF [BibTex]

Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning

Kuss, M.

Biologische Kybernetik, Technische Universität Darmstadt, Darmstadt, Germany, March 2006, passed with distinction, published online (phdthesis)

PDF [BibTex]

Kuss, M. Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning Biologische Kybernetik, Technische Universität Darmstadt, Darmstadt, Germany, March 2006, passed with distinction, published online (phdthesis)

PDF [BibTex]

Semigroups applied to transport and queueing processes

Radl, A.

Biologische Kybernetik, Eberhard Karls Universität, Tübingen, 2006 (phdthesis)

PDF [BibTex]

Radl, A. Semigroups applied to transport and queueing processes Biologische Kybernetik, Eberhard Karls Universität, Tübingen, 2006 (phdthesis)

PDF [BibTex]

Local Alignment Kernels for Protein Homology Detection

Saigo, H.

Biologische Kybernetik, Kyoto University, Kyoto, Japan, 2006 (phdthesis)

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Saigo, H. Local Alignment Kernels for Protein Homology Detection Biologische Kybernetik, Kyoto University, Kyoto, Japan, 2006 (phdthesis)

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2002

Nonlinear Multivariate Analysis with Geodesic Kernels

Kuss, M.

Biologische Kybernetik, Technische Universität Berlin, February 2002 (diplomathesis)

GZIP [BibTex]

2002

Kuss, M. Nonlinear Multivariate Analysis with Geodesic Kernels Biologische Kybernetik, Technische Universität Berlin, February 2002 (diplomathesis)

GZIP [BibTex]

Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms

Bousquet, O.

Biologische Kybernetik, Ecole Polytechnique, 2002 (phdthesis) Accepted

Abstract

New classification algorithms based on the notion of 'margin' (e.g. Support Vector Machines, Boosting) have recently been developed. The goal of this thesis is to better understand how they work, via a study of their theoretical performance. In order to do this, a general framework for real-valued classification is proposed. In this framework, it appears that the natural tools to use are Concentration Inequalities and Empirical Processes Theory. Thanks to an adaptation of these tools, a new measure of the size of a class of functions is introduced, which can be computed from the data. This allows, on the one hand, to better understand the role of eigenvalues of the kernel matrix in Support Vector Machines, and on the other hand, to obtain empirical model selection criteria.

PostScript [BibTex]

Bousquet, O. Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms Biologische Kybernetik, Ecole Polytechnique, 2002 (phdthesis) Accepted

PostScript [BibTex]

Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge

Chapelle, O.

Biologische Kybernetik, 2002 (phdthesis)

Abstract

This thesis presents a theoretical and practical study of Support Vector Machines (SVM) and related learning algorithms. In a first part, we introduce a new induction principle from which SVMs can be derived, but some new algorithms are also presented in this framework. In a second part, after studying how to estimate the generalization error of an SVM, we suggest to choose the kernel parameters of an SVM by minimizing this estimate. Several applications such as feature selection are presented. Finally the third part deals with the incoporation of prior knowledge in a learning algorithm and more specifically, we studied the case of known invariant transormations and the use of unlabeled data.

GZIP [BibTex]

Chapelle, O. Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge Biologische Kybernetik, 2002 (phdthesis)

GZIP [BibTex]

2001

Variationsverfahren zur Untersuchung von Grundzustandseigenschaften des Ein-Band Hubbard-Modells

Eichhorn, J.

Biologische Kybernetik, Technische Universität Dresden, Dresden/Germany, May 2001 (diplomathesis)

Abstract

Using different modifications of a new variational approach, statical groundstate properties of the one-band Hubbard model such as energy and staggered magnetisation are calculated. By taking into account additional fluctuations, the method ist gradually improved so that a very good description of the energy in one and two dimensions can be achieved. After a detailed discussion of the application in one dimension, extensions for two dimensions are introduced. By use of a modified version of the variational ansatz in particular a description of the quantum phase transition for the magnetisation should be possible.

PostScript [BibTex]