Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

41 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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2013

Camera-specific Image Denoising

Schober, M.

Eberhard Karls Universität Tübingen, Germany, October 2013 (diplomathesis)

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2013

Schober, M. Camera-specific Image Denoising Eberhard Karls Universität Tübingen, Germany, October 2013 (diplomathesis)

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Modelling and Learning Approaches to Image Denoising

Burger, HC.

Eberhard Karls Universität Tübingen, Germany, 2013 (phdthesis)

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Burger, HC. Modelling and Learning Approaches to Image Denoising Eberhard Karls Universität Tübingen, Germany, 2013 (phdthesis)

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Linear mixed models for genome-wide association studies

Lippert, C.

University of Tübingen, Germany, 2013 (phdthesis)

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Lippert, C. Linear mixed models for genome-wide association studies University of Tübingen, Germany, 2013 (phdthesis)

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Modeling and Learning Complex Motor Tasks: A case study on Robot Table Tennis

Mülling, K.

Technical University Darmstadt, Germany, 2013 (phdthesis)

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Mülling, K. Modeling and Learning Complex Motor Tasks: A case study on Robot Table Tennis Technical University Darmstadt, Germany, 2013 (phdthesis)

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Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models

Wang, Z.

Technical University Darmstadt, Germany, 2013 (phdthesis)

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Wang, Z. Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models Technical University Darmstadt, Germany, 2013 (phdthesis)

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2010

Approximate Inference in Graphical Models

Hennig, P.

University of Cambridge, November 2010 (phdthesis)

Web [BibTex]

2010

Hennig, P. Approximate Inference in Graphical Models University of Cambridge, November 2010 (phdthesis)

Web [BibTex]

Bayesian Inference and Experimental Design for Large Generalised Linear Models

Nickisch, H.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, September 2010 (phdthesis)

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Nickisch, H. Bayesian Inference and Experimental Design for Large Generalised Linear Models Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, September 2010 (phdthesis)

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Inferring High-Dimensional Causal Relations using Free Probability Theory

Zscheischler, J.

Humboldt Universität Berlin, Germany, August 2010 (diplomathesis)

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Zscheischler, J. Inferring High-Dimensional Causal Relations using Free Probability Theory Humboldt Universität Berlin, Germany, August 2010 (diplomathesis)

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Predictive Representations For Sequential Decision Making Under Uncertainty

Boularias, A.

Université Laval, Quebec, Canada, July 2010 (phdthesis)

Abstract

The problem of making decisions is ubiquitous in life. This problem becomes even more complex when the decisions should be made sequentially. In fact, the execution of an action at a given time leads to a change in the environment of the problem, and this change cannot be predicted with certainty. The aim of a decision-making process is to optimally select actions in an uncertain environment. To this end, the environment is often modeled as a dynamical system with multiple states, and the actions are executed so that the system evolves toward a desirable state. In this thesis, we proposed a family of stochastic models and algorithms in order to improve the quality of of the decision-making process. The proposed models are alternative to Markov Decision Processes, a largely used framework for this type of problems. In particular, we showed that the state of a dynamical system can be represented more compactly if it is described in terms of predictions of certain future events. We also showed that even the cognitive process of selecting actions, known as policy, can be seen as a dynamical system. Starting from this observation, we proposed a panoply of algorithms, all based on predictive policy representations, in order to solve different problems of decision-making, such as decentralized planning, reinforcement learning, or imitation learning. We also analytically and empirically demonstrated that the proposed approaches lead to a decrease in the computational complexity and an increase in the quality of the decisions, compared to standard approaches for planning and learning under uncertainty.

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Boularias, A. Predictive Representations For Sequential Decision Making Under Uncertainty Université Laval, Quebec, Canada, July 2010 (phdthesis)

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Semi-supervised Subspace Learning and Application to Human Functional Magnetic Brain Resonance Imaging Data

Shelton, J.

Biologische Kybernetik, Eberhard Karls Universität, Tübingen, Germany, July 2010 (diplomathesis)

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Shelton, J. Semi-supervised Subspace Learning and Application to Human Functional Magnetic Brain Resonance Imaging Data Biologische Kybernetik, Eberhard Karls Universität, Tübingen, Germany, July 2010 (diplomathesis)

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Quantitative Evaluation of MR-based Attenuation Correction for Positron Emission Tomography (PET)

Mantlik, F.

Biologische Kybernetik, Universität Mannheim, Germany, March 2010 (diplomathesis)

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Mantlik, F. Quantitative Evaluation of MR-based Attenuation Correction for Positron Emission Tomography (PET) Biologische Kybernetik, Universität Mannheim, Germany, March 2010 (diplomathesis)

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Finding Gene-Gene Interactions using Support Vector Machines

Rakitsch, B.

Eberhard Karls Universität Tübingen, Germany, 2010 (diplomathesis)

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Rakitsch, B. Finding Gene-Gene Interactions using Support Vector Machines Eberhard Karls Universität Tübingen, Germany, 2010 (diplomathesis)

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Accurate Prediction of Protein-Coding Genes with Discriminative Learning Techniques

Schweikert, G.

Technische Universität Berlin, Germany, 2010 (phdthesis)

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Schweikert, G. Accurate Prediction of Protein-Coding Genes with Discriminative Learning Techniques Technische Universität Berlin, Germany, 2010 (phdthesis)

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Quantitative Positron Emission Tomography with a Combined PET/MR System

Hofmann, M.

University of Oxford, UK, 2010 (phdthesis)

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Hofmann, M. Quantitative Positron Emission Tomography with a Combined PET/MR System University of Oxford, UK, 2010 (phdthesis)

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Structural and Relational Data Mining for Systems Biology Applications

Georgii, E.

Eberhard Karls Universität Tübingen, Germany , 2010 (phdthesis)

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Georgii, E. Structural and Relational Data Mining for Systems Biology Applications Eberhard Karls Universität Tübingen, Germany , 2010 (phdthesis)

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Population Coding in the Visual System: Statistical Methods and Theory

Macke, J.

Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

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Macke, J. Population Coding in the Visual System: Statistical Methods and Theory Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

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Bayesian Methods for Neural Data Analysis

Gerwinn, S.

Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

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Gerwinn, S. Bayesian Methods for Neural Data Analysis Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

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Clustering with Neighborhood Graphs

Maier, M.

Universität des Saarlandes, Saarbrücken, Germany, 2010 (phdthesis)

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Maier, M. Clustering with Neighborhood Graphs Universität des Saarlandes, Saarbrücken, Germany, 2010 (phdthesis)

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Detecting and modeling time shifts in microarray time series data applying Gaussian processes

Zwießele, M.

Eberhard Karls Universität Tübingen, Germany, 2010 (thesis)

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Zwießele, M. Detecting and modeling time shifts in microarray time series data applying Gaussian processes Eberhard Karls Universität Tübingen, Germany, 2010 (thesis)

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Detecting the mincut in sparse random graphs

Köhler, R.

Eberhard Karls Universität Tübingen, Germany, 2010 (diplomathesis)

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Köhler, R. Detecting the mincut in sparse random graphs Eberhard Karls Universität Tübingen, Germany, 2010 (diplomathesis)

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A wider view on encoding and decoding in the visual brain-computer interface speller system

Martens, S.

Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

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Martens, S. A wider view on encoding and decoding in the visual brain-computer interface speller system Eberhard Karls Universität Tübingen, Germany, 2010 (phdthesis)

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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.

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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)

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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)

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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.

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

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Kernel PCA for Image Compression

Huhle, B.

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

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

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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)

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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)

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Semigroups applied to transport and queueing processes

Radl, A.

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

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

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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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2003

Real-Time Face Detection

Kienzle, W.

Biologische Kybernetik, Eberhard-Karls-Universitaet Tuebingen, Tuebingen, Germany, October 2003 (diplomathesis)

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2003

Kienzle, W. Real-Time Face Detection Biologische Kybernetik, Eberhard-Karls-Universitaet Tuebingen, Tuebingen, Germany, October 2003 (diplomathesis)

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Ladungsträgerdynamik in optisch angeregten GaAs-Quantendrähten:Relaxation und Transport

Pfingsten, T.

Biologische Kybernetik, Institut für Festkörpertheorie, WWU Münster, June 2003 (diplomathesis)

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Pfingsten, T. Ladungsträgerdynamik in optisch angeregten GaAs-Quantendrähten:Relaxation und Transport Biologische Kybernetik, Institut für Festkörpertheorie, WWU Münster, June 2003 (diplomathesis)

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Kernel Methods for Classification and Signal Separation

Gretton, A.

pages: 226, Biologische Kybernetik, University of Cambridge, Cambridge, April 2003 (phdthesis)

PostScript [BibTex]

Gretton, A. Kernel Methods for Classification and Signal Separation pages: 226, Biologische Kybernetik, University of Cambridge, Cambridge, April 2003 (phdthesis)

PostScript [BibTex]