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2012


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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]


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Coregistration: Supplemental Material

Hirshberg, D., Loper, M., Rachlin, E., Black, M. J.

(No. 4), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

ps

pdf [BibTex]

pdf [BibTex]


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Lie Bodies: A Manifold Representation of 3D Human Shape. Supplemental Material

Freifeld, O., Black, M. J.

(No. 5), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

ps

pdf Project Page [BibTex]

pdf Project Page [BibTex]


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MPI-Sintel Optical Flow Benchmark: Supplemental Material

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.

(No. 6), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

ps

pdf Project Page [BibTex]

pdf Project Page [BibTex]


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From Pixels to Layers: Joint Motion Estimation and Segmentation

Sun, D.

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

ps

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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High Gamma-Power Predicts Performance in Brain-Computer Interfacing

Grosse-Wentrup, M., Schölkopf, B.

(3), Max-Planck-Institut für Intelligente Systeme, Tübingen, February 2012 (techreport)

Abstract
Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency gamma-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this nding as empirical support for an in uence of attentional networks on BCI-performance via modulation of the sensorimotor rhythm.

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]

2004


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Joint Kernel Maps

Weston, J., Schölkopf, B., Bousquet, O., Mann, .., Noble, W.

(131), Max-Planck-Institute for Biological Cybernetics, Tübingen, November 2004 (techreport)

ei

PDF [BibTex]

2004


PDF [BibTex]


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Semi-Supervised Induction

Yu, K., Tresp, V., Zhou, D.

(141), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, August 2004 (techreport)

Abstract
Considerable progress was recently achieved on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabelled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper investigates learning methods that effectively make use of both labelled and unlabelled data to build predictive functions, which are defined on not just the seen inputs but the whole space. As a nice property, the proposed method allows effcient training and can easily handle new test points. We validate the method based on both toy data and real world data sets.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Object categorization with SVM: kernels for local features

Eichhorn, J., Chapelle, O.

(137), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract
In this paper, we propose to combine an efficient image representation based on local descriptors with a Support Vector Machine classifier in order to perform object categorization. For this purpose, we apply kernels defined on sets of vectors. After testing different combinations of kernel / local descriptors, we have been able to identify a very performant one.

ei

PDF [BibTex]

PDF [BibTex]


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Hilbertian Metrics and Positive Definite Kernels on Probability Measures

Hein, M., Bousquet, O.

(126), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probability measures, continuing previous work. This type of kernels has shown very good results in text classification and has a wide range of possible applications. In this paper we extend the two-parameter family of Hilbertian metrics of Topsoe such that it now includes all commonly used Hilbertian metrics on probability measures. This allows us to do model selection among these metrics in an elegant and unified way. Second we investigate further our approach to incorporate similarity information of the probability space into the kernel. The analysis provides a better understanding of these kernels and gives in some cases a more efficient way to compute them. Finally we compare all proposed kernels in two text and one image classification problem.

ei

PDF [BibTex]

PDF [BibTex]


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Kernels, Associated Structures and Generalizations

Hein, M., Bousquet, O.

(127), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract
This paper gives a survey of results in the mathematical literature on positive definite kernels and their associated structures. We concentrate on properties which seem potentially relevant for Machine Learning and try to clarify some results that have been misused in the literature. Moreover we consider different lines of generalizations of positive definite kernels. Namely we deal with operator-valued kernels and present the general framework of Hilbertian subspaces of Schwartz which we use to introduce kernels which are distributions. Finally indefinite kernels and their associated reproducing kernel spaces are considered.

ei

PDF [BibTex]

PDF [BibTex]


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Kamerakalibrierung und Tiefenschätzung: Ein Vergleich von klassischer Bündelblockausgleichung und statistischen Lernalgorithmen

Sinz, FH.

Wilhelm-Schickard-Institut für Informatik, Universität Tübingen, Tübingen, Germany, March 2004 (techreport)

Abstract
Die Arbeit verleicht zwei Herangehensweisen an das Problem der Sch{\"a}tzung der r{\"a}umliche Position eines Punktes aus den Bildkoordinaten in zwei verschiedenen Kameras. Die klassische Methode der B{\"u}ndelblockausgleichung modelliert zwei Einzelkameras und sch{\"a}tzt deren {\"a}ußere und innere Orientierung mit einer iterativen Kalibrationsmethode, deren Konvergenz sehr stark von guten Startwerten abh{\"a}ngt. Die Tiefensch{\"a}tzung eines Punkts geschieht durch die Invertierung von drei der insgesamt vier Projektionsgleichungen der Einzalkameramodelle. Die zweite Methode benutzt Kernel Ridge Regression und Support Vector Regression, um direkt eine Abbildung von den Bild- auf die Raumkoordinaten zu lernen. Die Resultate zeigen, daß der Ansatz mit maschinellem Lernen, neben einer erheblichen Vereinfachung des Kalibrationsprozesses, zu h{\"o}heren Positionsgenaugikeiten f{\"u}hren kann.

ei

PDF [BibTex]

PDF [BibTex]


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Multivariate Regression with Stiefel Constraints

Bakir, G., Gretton, A., Franz, M., Schölkopf, B.

(128), MPI for Biological Cybernetics, Spemannstr 38, 72076, Tuebingen, 2004 (techreport)

Abstract
We introduce a new framework for regression between multi-dimensional spaces. Standard methods for solving this problem typically reduce the problem to one-dimensional regression by choosing features in the input and/or output spaces. These methods, which include PLS (partial least squares), KDE (kernel dependency estimation), and PCR (principal component regression), select features based on different a-priori judgments as to their relevance. Moreover, loss function and constraints are chosen not primarily on statistical grounds, but to simplify the resulting optimisation. By contrast, in our approach the feature construction and the regression estimation are performed jointly, directly minimizing a loss function that we specify, subject to a rank constraint. A major advantage of this approach is that the loss is no longer chosen according to the algorithmic requirements, but can be tailored to the characteristics of the task at hand; the features will then be optimal with respect to this objective. Our approach also allows for the possibility of using a regularizer in the optimization. Finally, by processing the observations sequentially, our algorithm is able to work on large scale problems.

ei

PDF [BibTex]

PDF [BibTex]


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Learning from Labeled and Unlabeled Data Using Random Walks

Zhou, D., Schölkopf, B.

Max Planck Institute for Biological Cybernetics, 2004 (techreport)

Abstract
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to predict the labels of the unlabeled points. Any supervised learning algorithm can be applied to this problem, for instance, Support Vector Machines (SVMs). The problem of our interest is if we can implement a classifier which uses the unlabeled data information in some way and has higher accuracy than the classifiers which use the labeled data only. Recently we proposed a simple algorithm, which can substantially benefit from large amounts of unlabeled data and demonstrates clear superiority to supervised learning methods. In this paper we further investigate the algorithm using random walks and spectral graph theory, which shed light on the key steps in this algorithm.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Behaviour and Convergence of the Constrained Covariance

Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Schölkopf, B., Logothetis, N.

(130), MPI for Biological Cybernetics, 2004 (techreport)

Abstract
We discuss reproducing kernel Hilbert space (RKHS)-based measures of statistical dependence, with emphasis on constrained covariance (COCO), a novel criterion to test dependence of random variables. We show that COCO is a test for independence if and only if the associated RKHSs are universal. That said, no independence test exists that can distinguish dependent and independent random variables in all circumstances. Dependent random variables can result in a COCO which is arbitrarily close to zero when the source densities are highly non-smooth, which can make dependence hard to detect empirically. All current kernel-based independence tests share this behaviour. Finally, we demonstrate exponential convergence between the population and empirical COCO, which implies that COCO does not suffer from slow learning rates when used as a dependence test.

ei

PDF [BibTex]

PDF [BibTex]


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Statistical Learning with Similarity and Dissimilarity Functions

von Luxburg, U.

pages: 1-166, Technische Universität Berlin, Germany, Technische Universität Berlin, Germany, 2004 (phdthesis)

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Confidence Sets for Ratios: A Purely Geometric Approach To Fieller’s Theorem

von Luxburg, U., Franz, V.

(133), Max Planck Institute for Biological Cybernetics, 2004 (techreport)

Abstract
We present a simple, geometric method to construct Fieller's exact confidence sets for ratios of jointly normally distributed random variables. Contrary to previous geometric approaches in the literature, our method is valid in the general case where both sample mean and covariance are unknown. Moreover, not only the construction but also its proof are purely geometric and elementary, thus giving intuition into the nature of the confidence sets.

ei

PDF [BibTex]

PDF [BibTex]


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Transductive Inference with Graphs

Zhou, D., Schölkopf, B.

Max Planck Institute for Biological Cybernetics, 2004, See the improved version Regularization on Discrete Spaces. (techreport)

Abstract
We propose a general regularization framework for transductive inference. The given data are thought of as a graph, where the edges encode the pairwise relationships among data. We develop discrete analysis and geometry on graphs, and then naturally adapt the classical regularization in the continuous case to the graph situation. A new and effective algorithm is derived from this general framework, as well as an approach we developed before.

ei

[BibTex]

[BibTex]


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Classification and Feature Extraction in Man and Machine

Graf, AAB.

Biologische Kybernetik, University of Tübingen, Germany, 2004, online publication (phdthesis)

ei

[BibTex]

[BibTex]


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Investigation of oxide layers in tunnel junctions

Amaladass, E. P.

University of Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


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Untersuchung der Desorptionskinetik von Metallhydriden in Bezug auf technische Anwendungen

von Zeppelin, F.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

[BibTex]

[BibTex]


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Towards Tractable Parameter-Free Statistical Learning (Phd Thesis)

D’Souza, A

Department of Computer Science, University of Southern California, Los Angeles, 2004, clmc (phdthesis)

am

link (url) [BibTex]

link (url) [BibTex]


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Dynamik von Wasserstoff in nanokristallinen Systemen

Stanik, E.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

[BibTex]

[BibTex]


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Inselwachstum auf Festkörperoberflächen unter Ionenbestrahlung

Frank, A.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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Flusslinienverankerung in HTSL-Schichten mit kontrollierter Defektstruktur im Nanometerbereich

Leonhardt, S.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

[BibTex]

[BibTex]


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Investigation of the stability of metals on polymers

Amoako, G.

University of Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


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Flusslinienverankerung in Hochtemperatursupraleitern auf nanostrukturierten Substraten

Brück, S.

Universität Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


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Ionenstreuung mit Monolagen-Tiefenauflösung

Olliges, S.

Universität Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

[BibTex]


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Untersuchungen zum Magnetismus von Clustern und Nanopartikeln und zum Einfluss der Wechselwirkung mit ihrer Umgebung

Fauth, Kai

Julius-Maximilians-Universität Würzburg, Würzburg, 2004 (phdthesis)

mms

[BibTex]

[BibTex]

2001


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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.

ei

PostScript [BibTex]

2001


PostScript [BibTex]


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Inference Principles and Model Selection

Buhmann, J., Schölkopf, B.

(01301), Dagstuhl Seminar, 2001 (techreport)

ei

Web [BibTex]

Web [BibTex]


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Kritische Ströme über Kleinwinkelkorngrenzen in YBCO

Albrecht, J.

Universität Stuttgart, Stuttgart, 2001 (phdthesis)

mms

[BibTex]

[BibTex]


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Von der elektronischen Struktur zum makroskopischen Verhalten: Eine Multi-Skalen Analyse der Plastizität

Kohlhammer, S.

Universität Stuttgart, Stuttgart, 2001 (phdthesis)

mms

[BibTex]


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Kernspinresonanzuntersuchungen zur Diffusion von Wasserstoff in den Di- und Trihydriden der Übergangsmetalle

Gottwald, J.

Universität Stuttgart, Stuttgart, 2001 (phdthesis)

mms

[BibTex]