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2015


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

Grimm, Dominik

Eberhard Karls Universität Tübingen, November 2015 (phdthesis)

ei

[BibTex]

2015


[BibTex]


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Causal Discovery Beyond Conditional Independences

Sgouritsa, E.

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

ei

link (url) [BibTex]

link (url) [BibTex]


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Gaussian Process Optimization for Self-Tuning Control

Marco, A.

Polytechnic University of Catalonia (BarcelonaTech), October 2015 (mastersthesis)

am ics

PDF Project Page [BibTex]

PDF Project Page [BibTex]


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Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism

Besserve, M.

53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

ei

[BibTex]

[BibTex]


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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)

ei

[BibTex]

[BibTex]


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Machine Learning Approaches to Image Deconvolution

Schuler, C.

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

ei

[BibTex]

[BibTex]


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Adaptive and Learning Concepts in Hydraulic Force Control

Doerr, A.

University of Stuttgart, September 2015 (mastersthesis)

am ics

[BibTex]

[BibTex]


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Object Detection Using Deep Learning - Learning where to search using visual attention

Kloss, A.

Eberhard Karls Universität Tübingen, May 2015 (mastersthesis)

Abstract
Detecting and identifying the different objects in an image fast and reliably is an important skill for interacting with one’s environment. The main problem is that in theory, all parts of an image have to be searched for objects on many different scales to make sure that no object instance is missed. It however takes considerable time and effort to actually classify the content of a given image region and both time and computational capacities that an agent can spend on classification are limited. Humans use a process called visual attention to quickly decide which locations of an image need to be processed in detail and which can be ignored. This allows us to deal with the huge amount of visual information and to employ the capacities of our visual system efficiently. For computer vision, researchers have to deal with exactly the same problems, so learning from the behaviour of humans provides a promising way to improve existing algorithms. In the presented master’s thesis, a model is trained with eye tracking data recorded from 15 participants that were asked to search images for objects from three different categories. It uses a deep convolutional neural network to extract features from the input image that are then combined to form a saliency map. This map provides information about which image regions are interesting when searching for the given target object and can thus be used to reduce the parts of the image that have to be processed in detail. The method is based on a recent publication of Kümmerer et al., but in contrast to the original method that computes general, task independent saliency, the presented model is supposed to respond differently when searching for different target categories.

am

PDF Project Page [BibTex]


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Blind Retrospective Motion Correction of MR Images

Loktyushin, A.

University of Tübingen, Germany, May 2015 (phdthesis)

ei

[BibTex]

[BibTex]


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Robot Arm Tracking with Random Decision Forests

Widmaier, F.

Eberhard-Karls-Universität Tübingen, May 2015 (mastersthesis)

Abstract
For grasping and manipulation with robot arms, knowing the current pose of the arm is crucial for successful controlling its motion. Often, pose estimations can be acquired from encoders inside the arm, but they can have significant inaccuracy which makes the use of additional techniques necessary. In this master thesis, a novel approach of robot arm pose estimation is presented, that works on single depth images without the need of prior foreground segmentation or other preprocessing steps. A random regression forest is used, which is trained only on synthetically generated data. The approach improves former work by Bohg et al. by considerably reducing the computational effort both at training and test time. The forest in the new method directly estimates the desired joint angles while in the former approach, the forest casts 3D position votes for the joints, which then have to be clustered and fed into an iterative inverse kinematic process to finally get the joint angles. To improve the estimation accuracy, the standard training objective of the forest training is replaced by a specialized function that makes use of a model-dependent distance metric, called DISP. Experimental results show that the specialized objective indeed improves pose estimation and it is shown that the method, despite of being trained on synthetic data only, is able to provide reasonable estimations for real data at test time.

am

PDF Project Page [BibTex]

PDF Project Page [BibTex]


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Shape Models of the Human Body for Distributed Inference

Zuffi, S.

Brown University, May 2015 (phdthesis)

Abstract
In this thesis we address the problem of building shape models of the human body, in 2D and 3D, which are realistic and efficient to use. We focus our efforts on the human body, which is highly articulated and has interesting shape variations, but the approaches we present here can be applied to generic deformable and articulated objects. To address efficiency, we constrain our models to be part-based and have a tree-structured representation with pairwise relationships between connected parts. This allows the application of methods for distributed inference based on message passing. To address realism, we exploit recent advances in computer graphics that represent the human body with statistical shape models learned from 3D scans. We introduce two articulated body models, a 2D model, named Deformable Structures (DS), which is a contour-based model parameterized for 2D pose and projected shape, and a 3D model, named Stitchable Puppet (SP), which is a mesh-based model parameterized for 3D pose, pose-dependent deformations and intrinsic body shape. We have successfully applied the models to interesting and challenging problems in computer vision and computer graphics, namely pose estimation from static images, pose estimation from video sequences, pose and shape estimation from 3D scan data. This advances the state of the art in human pose and shape estimation and suggests that carefully de ned realistic models can be important for computer vision. More work at the intersection of vision and graphics is thus encouraged.

ps

PDF [BibTex]


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Independence of cause and mechanism in brain networks

Besserve, M.

DALI workshop on Networks: Processes and Causality, April 2015 (talk)

ei

[BibTex]

[BibTex]


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From Scans to Models: Registration of 3D Human Shapes Exploiting Texture Information

Bogo, F.

University of Padova, March 2015 (phdthesis)

Abstract
New scanning technologies are increasing the importance of 3D mesh data, and of algorithms that can reliably register meshes obtained from multiple scans. Surface registration is important e.g. for building full 3D models from partial scans, identifying and tracking objects in a 3D scene, creating statistical shape models. Human body registration is particularly important for many applications, ranging from biomedicine and robotics to the production of movies and video games; but obtaining accurate and reliable registrations is challenging, given the articulated, non-rigidly deformable structure of the human body. In this thesis, we tackle the problem of 3D human body registration. We start by analyzing the current state of the art, and find that: a) most registration techniques rely only on geometric information, which is ambiguous on flat surface areas; b) there is a lack of adequate datasets and benchmarks in the field. We address both issues. Our contribution is threefold. First, we present a model-based registration technique for human meshes that combines geometry and surface texture information to provide highly accurate mesh-to-mesh correspondences. Our approach estimates scene lighting and surface albedo, and uses the albedo to construct a high-resolution textured 3D body model that is brought into registration with multi-camera image data using a robust matching term. Second, by leveraging our technique, we present FAUST (Fine Alignment Using Scan Texture), a novel dataset collecting 300 high-resolution scans of 10 people in a wide range of poses. FAUST is the first dataset providing both real scans and automatically computed, reliable "ground-truth" correspondences between them. Third, we explore possible uses of our approach in dermatology. By combining our registration technique with a melanocytic lesion segmentation algorithm, we propose a system that automatically detects new or evolving lesions over almost the entire body surface, thus helping dermatologists identify potential melanomas. We conclude this thesis investigating the benefits of using texture information to establish frame-to-frame correspondences in dynamic monocular sequences captured with consumer depth cameras. We outline a novel approach to reconstruct realistic body shape and appearance models from dynamic human performances, and show preliminary results on challenging sequences captured with a Kinect.

ps

[BibTex]


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Long Range Motion Estimation and Applications

Sevilla-Lara, L.

Long Range Motion Estimation and Applications, University of Massachusetts Amherst, University of Massachusetts Amherst, Febuary 2015 (phdthesis)

Abstract
Finding correspondences between images underlies many computer vision problems, such as optical flow, tracking, stereovision and alignment. Finding these correspondences involves formulating a matching function and optimizing it. This optimization process is often gradient descent, which avoids exhaustive search, but relies on the assumption of being in the basin of attraction of the right local minimum. This is often the case when the displacement is small, and current methods obtain very accurate results for small motions. However, when the motion is large and the matching function is bumpy this assumption is less likely to be true. One traditional way of avoiding this abruptness is to smooth the matching function spatially by blurring the images. As the displacement becomes larger, the amount of blur required to smooth the matching function becomes also larger. This averaging of pixels leads to a loss of detail in the image. Therefore, there is a trade-off between the size of the objects that can be tracked and the displacement that can be captured. In this thesis we address the basic problem of increasing the size of the basin of attraction in a matching function. We use an image descriptor called distribution fields (DFs). By blurring the images in DF space instead of in pixel space, we in- crease the size of the basin attraction with respect to traditional methods. We show competitive results using DFs both in object tracking and optical flow. Finally we demonstrate an application of capturing large motions for temporal video stitching.

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[BibTex]

[BibTex]


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Policy Search for Imitation Learning

Doerr, A.

University of Stuttgart, January 2015 (thesis)

am ics

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Information-Theoretic Implications of Classical and Quantum Causal Structures

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.

18th Conference on Quantum Information Processing (QIP), 2015 (talk)

ei

Web link (url) [BibTex]

Web link (url) [BibTex]


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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)

ei

[BibTex]

[BibTex]


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Sequential Image Deconvolution Using Probabilistic Linear Algebra

Gao, M.

Technical University of Munich, Germany, 2015 (mastersthesis)

ei

[BibTex]

[BibTex]


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Causal Inference in Neuroimaging

Casarsa de Azevedo, L.

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

ei

[BibTex]

[BibTex]


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The effect of frowning on attention

Ibarra Chaoul, A.

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

ei

[BibTex]

[BibTex]


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The search for single exoplanet transits in the Kepler light curves

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

ei

link (url) [BibTex]

link (url) [BibTex]


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Strukturelle und spektroskopische Eigenschaften epitaktischer FeMn/Co Exchange-Bias-Systeme

Schmidt, M.

Universität Stuttgart, Stuttgart, 2015 (phdthesis)

mms

link (url) DOI [BibTex]


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Ultraschnelles Vortexkernschalten

Noske, M.

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2015 (phdthesis)

mms

[BibTex]

[BibTex]


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Investigations of unusual hard magnetic MnBi LTP phase, utilizing temperature dependent SQUID-FORC

Muralidhar, Shreyas

Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

mms

[BibTex]

[BibTex]


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Magnetische Röntgenmikroskopie an Hochtemperatur-Supraleitern

Stahl, C.

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2015 (phdthesis)

mms

[BibTex]

[BibTex]


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Voltage-induced magnetic manipulation of a microstructured iron gold multilayer system

Sittig, Robert

Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

mms

[BibTex]

[BibTex]


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Transfer of angular momentum from the spin system to the lattice during ultrafast magnetization

Tsatsoulis, T.

Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

mms

[BibTex]

[BibTex]


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Derivation of phenomenological expressions for transition matrix elements for electron-phonon scattering

Illg, C., Haag, M., Müller, B. Y., Czycholl, G., Fähnle, M.

2015 (misc)

mms

link (url) [BibTex]


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Quantum kinetic theory of ultrafast demagnetization by electron-phonon scattering

Briones Paz, J. Z.

Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

mms

[BibTex]

[BibTex]

2008


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BCPy2000

Hill, N., Schreiner, T., Puzicha, C., Farquhar, J.

Workshop "Machine Learning Open-Source Software" at NIPS, December 2008 (talk)

ei

Web [BibTex]

2008


Web [BibTex]


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Logistic Regression for Graph Classification

Shervashidze, N., Tsuda, K.

NIPS Workshop on "Structured Input - Structured Output" (NIPS SISO), December 2008 (talk)

Abstract
In this paper we deal with graph classification. We propose a new algorithm for performing sparse logistic regression for graphs, which is comparable in accuracy with other methods of graph classification and produces probabilistic output in addition. Sparsity is required for the reason of interpretability, which is often necessary in domains such as bioinformatics or chemoinformatics.

ei

Web [BibTex]

Web [BibTex]


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New Projected Quasi-Newton Methods with Applications

Sra, S.

Microsoft Research Tech-talk, December 2008 (talk)

Abstract
Box-constrained convex optimization problems are central to several applications in a variety of fields such as statistics, psychometrics, signal processing, medical imaging, and machine learning. Two fundamental examples are the non-negative least squares (NNLS) problem and the non-negative Kullback-Leibler (NNKL) divergence minimization problem. The non-negativity constraints are usually based on an underlying physical restriction, for e.g., when dealing with applications in astronomy, tomography, statistical estimation, or image restoration, the underlying parameters represent physical quantities such as concentration, weight, intensity, or frequency counts and are therefore only interpretable with non-negative values. Several modern optimization methods can be inefficient for simple problems such as NNLS and NNKL as they are really designed to handle far more general and complex problems. In this work we develop two simple quasi-Newton methods for solving box-constrained (differentiable) convex optimization problems that utilize the well-known BFGS and limited memory BFGS updates. We position our method between projected gradient (Rosen, 1960) and projected Newton (Bertsekas, 1982) methods, and prove its convergence under a simple Armijo step-size rule. We illustrate our method by showing applications to: Image deblurring, Positron Emission Tomography (PET) image reconstruction, and Non-negative Matrix Approximation (NMA). On medium sized data we observe performance competitive to established procedures, while for larger data the results are even better.

ei

PDF [BibTex]

PDF [BibTex]


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MR-Based PET Attenuation Correction: Initial Results for Whole Body

Hofmann, M., Steinke, F., Aschoff, P., Lichy, M., Brady, M., Schölkopf, B., Pichler, B.

Medical Imaging Conference, October 2008 (talk)

ei

[BibTex]

[BibTex]


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Nonparametric Indepedence Tests: Space Partitioning and Kernel Approaches

Gretton, A., Györfi, L.

19th International Conference on Algorithmic Learning Theory (ALT08), October 2008 (talk)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Reinforcement Learning for Motor Primitives

Kober, J.

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

ei

PDF [BibTex]

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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mGene: A Novel Discriminative Gene Finder

Schweikert, G., Zeller, G., Zien, A., Behr, J., Sonnenburg, S., Philips, P., Ong, C., Rätsch, G.

Worm Genomics and Systems Biology meeting, July 2008 (talk)

ei

[BibTex]

[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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Discovering Common Sequence Variation in Arabidopsis thaliana

Rätsch, G., Clark, R., Schweikert, G., Toomajian, C., Ossowski, S., Zeller, G., Shinn, P., Warthman, N., Hu, T., Fu, G., Hinds, D., Cheng, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Ecker, J., Weigel, D., Schneeberger, K., Bohlen, A.

16th Annual International Conference Intelligent Systems for Molecular Biology (ISMB), July 2008 (talk)

ei

Web [BibTex]

Web [BibTex]


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Coding Theory in Brain-Computer Interfaces

Martens, SMM.

Soria Summerschool on Computational Mathematics "Algebraic Coding Theory" (S3CM), July 2008 (talk)

ei

Web [BibTex]

Web [BibTex]


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Motor Skill Learning for Cognitive Robotics

Peters, J.

6th International Cognitive Robotics Workshop (CogRob), July 2008 (talk)

Abstract
Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this tutorial, we give a general overview on motor skill learning for cognitive robotics using research at ATR, USC, CMU and Max-Planck in order to illustrate the problems in motor skill learning. For doing so, we discuss task-appropriate representations and algorithms for learning robot motor skills. Among the topics are the learning basic movements or motor primitives by imitation and reinforcement learning, learning rhytmic and discrete movements, fast regression methods for learning inverse dynamics and setups for learning task-space policies. Examples on various robots, e.g., SARCOS DB, the SARCOS Master Arm, BDI Little Dog and a Barrett WAM, are shown and include Ball-in-a-Cup, T-Ball, Juggling, Devil-Sticking, Operational Space Control and many others.

ei

Web [BibTex]

Web [BibTex]


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Painless Embeddings of Distributions: the Function Space View (Part 1)

Fukumizu, K., Gretton, A., Smola, A.

25th International Conference on Machine Learning (ICML), July 2008 (talk)

Abstract
This tutorial will give an introduction to the recent understanding and methodology of the kernel method: dealing with higher order statistics by embedding painlessly random variables/probability distributions. In the early days of kernel machines research, the "kernel trick" was considered a useful way of constructing nonlinear algorithms from linear ones. More recently, however, it has become clear that a potentially more far reaching use of kernels is as a linear way of dealing with higher order statistics by embedding distributions in a suitable reproducing kernel Hilbert space (RKHS). Notably, unlike the straightforward expansion of higher order moments or conventional characteristic function approach, the use of kernels or RKHS provides a painless, tractable way of embedding distributions. This line of reasoning leads naturally to the questions: what does it mean to embed a distribution in an RKHS? when is this embedding injective (and thus, when do different distributions have unique mappings)? what implications are there for learning algorithms that make use of these embeddings? This tutorial aims at answering these questions. There are a great variety of applications in machine learning and computer science, which require distribution estimation and/or comparison.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Reinforcement Learning for Robotics

Peters, J.

8th European Workshop on Reinforcement Learning for Robotics (EWRL), July 2008 (talk)

ei

Web [BibTex]

Web [BibTex]


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Thin-Plate Splines Between Riemannian Manifolds

Steinke, F., Hein, M., Schölkopf, B.

Workshop on Geometry and Statistics of Shapes, June 2008 (talk)

Abstract
With the help of differential geometry we describe a framework to define a thin-plate spline like energy for maps between arbitrary Riemannian manifolds. The so-called Eells energy only depends on the intrinsic geometry of the input and output manifold, but not on their respective representation. The energy can then be used for regression between manifolds, we present results for cases where the outputs are rotations, sets of angles, or points on 3D surfaces. In the future we plan to also target regression where the output is an element of "shape space", understood as a Riemannian manifold. One could also further explore the meaning of the Eells energy when applied to diffeomorphisms between shapes, especially with regard to its potential use as a distance measure between shapes that does not depend on the embedding or the parametrisation of the shapes.

ei

Web [BibTex]

Web [BibTex]


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Learning resolved velocity control

Peters, J.

2008 IEEE International Conference on Robotics and Automation (ICRA), May 2008 (talk)

ei

Web [BibTex]

Web [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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Bayesian methods for protein structure determination

Habeck, M.

Machine Learning in Structural Bioinformatics, April 2008 (talk)

ei

Web [BibTex]

Web [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]