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2019


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Robot Learning for Muscular Robots

Büchler, D.

Technical University Darmstadt, Germany, December 2019 (phdthesis)

ei

[BibTex]

2019


[BibTex]


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Real Time Probabilistic Models for Robot Trajectories

Gomez-Gonzalez, S.

Technical University Darmstadt, Germany, December 2019 (phdthesis)

ei

[BibTex]

[BibTex]


Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems
Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems

Baumann, D.

KTH Royal Institute of Technology, Stockholm, Febuary 2019 (phdthesis)

ics

PDF [BibTex]

PDF [BibTex]


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Learning Transferable Representations

Rojas-Carulla, M.

University of Cambridge, UK, 2019 (phdthesis)

ei

[BibTex]

[BibTex]


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Sample-efficient deep reinforcement learning for continuous control

Gu, S.

University of Cambridge, UK, 2019 (phdthesis)

ei

[BibTex]


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Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing

Xu, J.

Technical University of Munich, Germany, 2019 (mastersthesis)

ei

[BibTex]

[BibTex]


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Novel X-ray lenses for direct and coherent imaging

Sanli, U. T.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

mms

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Quantification of tumor heterogeneity using PET/MRI and machine learning

Katiyar, P.

Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

ei

[BibTex]

[BibTex]


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Actively Learning Dynamical Systems with Gaussian Processes

Buisson-Fenet, M.

Mines ParisTech, PSL Research University, 2019 (mastersthesis)

Abstract
Predicting the behavior of complex systems is of great importance in many fields such as engineering, economics or meteorology. The evolution of such systems often follows a certain structure, which can be induced, for example from the laws of physics or of market forces. Mathematically, this structure is often captured by differential equations. The internal functional dependencies, however, are usually unknown. Hence, using machine learning approaches that recreate this structure directly from data is a promising alternative to designing physics-based models. In particular, for high dimensional systems with nonlinear effects, this can be a challenging task. Learning dynamical systems is different from the classical machine learning tasks, such as image processing, and necessitates different tools. Indeed, dynamical systems can be actuated, often by applying torques or voltages. Hence, the user has a power of decision over the system, and can drive it to certain states by going through the dynamics. Actuating this system generates data, from which a machine learning model of the dynamics can be trained. However, gathering informative data that is representative of the whole state space remains a challenging task. The question of active learning then becomes important: which control inputs should be chosen by the user so that the data generated during an experiment is informative, and enables efficient training of the dynamics model? In this context, Gaussian processes can be a useful framework for approximating system dynamics. Indeed, they perform well on small and medium sized data sets, as opposed to most other machine learning frameworks. This is particularly important considering data is often costly to generate and process, most of all when producing it involves actuating a complex physical system. Gaussian processes also yield a notion of uncertainty, which indicates how sure the model is about its predictions. In this work, we investigate in a principled way how to actively learn dynamical systems, by selecting control inputs that generate informative data. We model the system dynamics by a Gaussian process, and use information-theoretic criteria to identify control trajectories that maximize the information gain. Thus, the input space can be explored efficiently, leading to a data-efficient training of the model. We propose several methods, investigate their theoretical properties and compare them extensively in a numerical benchmark. The final method proves to be efficient at generating informative data. Thus, it yields the lowest prediction error with the same amount of samples on most benchmark systems. We propose several variants of this method, allowing the user to trade off computations with prediction accuracy, and show it is versatile enough to take additional objectives into account.

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[BibTex]

[BibTex]

2013


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Camera-specific Image Denoising

Schober, M.

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

ei pn

PDF [BibTex]

2013


PDF [BibTex]


Human Pose Calculation from Optical Flow Data
Human Pose Calculation from Optical Flow Data

Black, M., Loper, M., Romero, J., Zuffi, S.

European Patent Application EP 2843621 , August 2013 (patent)

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Google Patents [BibTex]

Google Patents [BibTex]


Statistics on Manifolds with Applications to Modeling Shape Deformations
Statistics on Manifolds with Applications to Modeling Shape Deformations

Freifeld, O.

Brown University, August 2013 (phdthesis)

Abstract
Statistical models of non-rigid deformable shape have wide application in many fi elds, including computer vision, computer graphics, and biometry. We show that shape deformations are well represented through nonlinear manifolds that are also matrix Lie groups. These pattern-theoretic representations lead to several advantages over other alternatives, including a principled measure of shape dissimilarity and a natural way to compose deformations. Moreover, they enable building models using statistics on manifolds. Consequently, such models are superior to those based on Euclidean representations. We demonstrate this by modeling 2D and 3D human body shape. Shape deformations are only one example of manifold-valued data. More generally, in many computer-vision and machine-learning problems, nonlinear manifold representations arise naturally and provide a powerful alternative to Euclidean representations. Statistics is traditionally concerned with data in a Euclidean space, relying on the linear structure and the distances associated with such a space; this renders it inappropriate for nonlinear spaces. Statistics can, however, be generalized to nonlinear manifolds. Moreover, by respecting the underlying geometry, the statistical models result in not only more e ffective analysis but also consistent synthesis. We go beyond previous work on statistics on manifolds by showing how, even on these curved spaces, problems related to modeling a class from scarce data can be dealt with by leveraging information from related classes residing in di fferent regions of the space. We show the usefulness of our approach with 3D shape deformations. To summarize our main contributions: 1) We de fine a new 2D articulated model -- more expressive than traditional ones -- of deformable human shape that factors body-shape, pose, and camera variations. Its high realism is obtained from training data generated from a detailed 3D model. 2) We defi ne a new manifold-based representation of 3D shape deformations that yields statistical deformable-template models that are better than the current state-of-the- art. 3) We generalize a transfer learning idea from Euclidean spaces to Riemannian manifolds. This work demonstrates the value of modeling manifold-valued data and their statistics explicitly on the manifold. Specifi cally, the methods here provide new tools for shape analysis.

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pdf Project Page [BibTex]


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Modelling and Learning Approaches to Image Denoising

Burger, HC.

Eberhard Karls Universität Tübingen, Germany, 2013 (phdthesis)

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[BibTex]

[BibTex]


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Linear mixed models for genome-wide association studies

Lippert, C.

University of Tübingen, Germany, 2013 (phdthesis)

ei

[BibTex]

[BibTex]


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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)

ei

[BibTex]

[BibTex]


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Intention Inference and Decision Making with Hierarchical Gaussian Process Dynamics Models

Wang, Z.

Technical University Darmstadt, Germany, 2013 (phdthesis)

ei

[BibTex]


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Quantum kinetic theory for demagnetization after femtosecond laser pulses

Teeny, N.

Universität Stuttgart, Stuttgart, 2013 (mastersthesis)

mms

[BibTex]

[BibTex]

2012


Virtual Human Bodies with Clothing and Hair: From Images to Animation
Virtual Human Bodies with Clothing and Hair: From Images to Animation

Guan, P.

Brown University, Department of Computer Science, December 2012 (phdthesis)

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pdf [BibTex]

2012


pdf [BibTex]


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Scalable graph kernels

Shervashidze, N.

Eberhard Karls Universität Tübingen, Germany, October 2012 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


From Pixels to Layers: Joint Motion Estimation and Segmentation
From Pixels to Layers: Joint Motion Estimation and Segmentation

Sun, D.

Brown University, Department of Computer Science, July 2012 (phdthesis)

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pdf [BibTex]

pdf [BibTex]


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Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J.

Technische Universität Darmstadt, Germany, March 2012 (phdthesis)

ei

PDF [BibTex]

PDF [BibTex]


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Structure and Dynamics of Diffusion Networks

Gomez Rodriguez, M.

Department of Electrical Engineering, Stanford University, 2012 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


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Blind Deconvolution in Scientific Imaging & Computational Photography

Hirsch, M.

Eberhard Karls Universität Tübingen, Germany, 2012 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


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Wasserstoffspeicherkapazität poröser Materialien in Kryoadsorptionstanks

Schlichtenmayer, M.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Behandlung stark nichtkollinearer Magnetisierungsstrukturen mit der Spin-Cluster-Entwicklung

Dietermann, F.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Spinwelleninduziertes Schalten magnetischer Vortexkerne

Kammerer, M.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Die Stabilität des stromtragenden Zustands in MgB2 Schichten mit modifizierter Mikrostruktur

Treiber, S.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

[BibTex]

[BibTex]


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Hartmagnetische L10-FePt basierte gro\ssflächige Nanomuster mittels Nanoimprint-Lithografie

Bublat, T.

Universität Stuttgart, Stuttgart, 2012 (phdthesis)

mms

[BibTex]

[BibTex]

2008


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Reinforcement Learning for Motor Primitives

Kober, J.

Biologische Kybernetik, University of Stuttgart, Stuttgart, Germany, August 2008 (diplomathesis)

ei

PDF [BibTex]

2008


PDF [BibTex]


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Asymmetries of Time Series under Inverting their Direction

Peters, J.

Biologische Kybernetik, University of Heidelberg, August 2008 (diplomathesis)

ei

PDF [BibTex]

PDF [BibTex]


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Learning an Interest Operator from Human Eye Movements

Kienzle, W.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, July 2008 (phdthesis)

ei

[BibTex]

[BibTex]


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Causal inference from statistical data

Sun, X.

Biologische Kybernetik, Technische Hochschule Karlsruhe, Karlsruhe, Germany, April 2008 (phdthesis)

ei

Web [BibTex]

Web [BibTex]


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Pairwise Correlations and Multineuronal Firing Patterns in Primary Visual Cortex

Berens, P.

Biologische Kybernetik, Eberhard Karls Universität Tübingen, Tübingen, Germany, April 2008 (diplomathesis)

ei

[BibTex]

[BibTex]


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Development and Application of a Python Scripting Framework for BCI2000

Schreiner, T.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, January 2008 (diplomathesis)

ei

[BibTex]

[BibTex]


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Efficient and Invariant Regularisation with Application to Computer Graphics

Walder, CJ.

Biologische Kybernetik, University of Queensland, Brisbane, Australia, January 2008 (phdthesis)

Abstract
This thesis develops the theory and practise of reproducing kernel methods. Many functional inverse problems which arise in, for example, machine learning and computer graphics, have been treated with practical success using methods based on a reproducing kernel Hilbert space perspective. This perspective is often theoretically convenient, in that many functional analysis problems reduce to linear algebra problems in these spaces. Somewhat more complex is the case of conditionally positive definite kernels, and we provide an introduction to both cases, deriving in a particularly elementary manner some key results for the conditionally positive definite case. A common complaint of the practitioner is the long running time of these kernel based algorithms. We provide novel ways of alleviating these problems by essentially using a non-standard function basis which yields computational advantages. That said, by doing so we must also forego the aforementioned theoretical conveniences, and hence need some additional analysis which we provide in order to make the approach practicable. We demonstrate that the method leads to state of the art performance on the problem of surface reconstruction from points. We also provide some analysis of kernels invariant to transformations such as translation and dilation, and show that this indicates the value of learning algorithms which use conditionally positive definite kernels. Correspondingly, we provide a few approaches for making such algorithms practicable. We do this either by modifying the kernel, or directly solving problems with conditionally positive definite kernels, which had previously only been solved with positive definite kernels. We demonstrate the advantage of this approach, in particular by attaining state of the art classification performance with only one free parameter.

ei

PDF [BibTex]

PDF [BibTex]


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Ab-initio Elektronentheorie der magnetischen Anisotropie im System FePt mit der Clusterentwicklungsmethode

Subkow, S.

Universität Stuttgart, Stuttgart, 2008 (mastersthesis)

mms

[BibTex]


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Magnetism of amorphous and highly anisotropic multilayer systems on flat substrates and nanospheres

Amaladass, E. P.

Universität Stuttgart, Stuttgart, 2008 (phdthesis)

mms

link (url) [BibTex]