Publications | Max Planck Institute for Intelligent Systems

107 results (View BibTeX file of all listed publications)

2015

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)

2015

ei Foreman-Mackey, D., Hogg, D. W., Schölkopf, B. The search for single exoplanet transits in the Kepler light curves IAU General Assembly, 22, pages: 2258352, 2015 (talk)

link (url) [BibTex]

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

Schmidt, M.

Universität Stuttgart, Stuttgart, 2015 (phdthesis)

mms

link (url) DOI [BibTex]

mms Schmidt, M. Strukturelle und spektroskopische Eigenschaften epitaktischer FeMn/Co Exchange-Bias-Systeme Universität Stuttgart, Stuttgart, 2015 (phdthesis)

link (url) DOI [BibTex]

Ultraschnelles Vortexkernschalten

Noske, M.

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

mms

[BibTex]

mms Noske, M. Ultraschnelles Vortexkernschalten Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2015 (phdthesis)

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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]

mms Muralidhar, Shreyas Investigations of unusual hard magnetic MnBi LTP phase, utilizing temperature dependent SQUID-FORC Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

[BibTex]

Magnetische Röntgenmikroskopie an Hochtemperatur-Supraleitern

Stahl, C.

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

mms

[BibTex]

mms Stahl, C. Magnetische Röntgenmikroskopie an Hochtemperatur-Supraleitern Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2015 (phdthesis)

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

Sittig, Robert

Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

mms

[BibTex]

mms Sittig, Robert Voltage-induced magnetic manipulation of a microstructured iron gold multilayer system Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

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Ion beam lithographic and multilayer Fresnel zone plates for soft and hard X-rays: Nanofabrication and characterization

Keskinbora, K.

Universität Stuttgart, Stuttgart, 2015 (phdthesis)

mms

link (url) DOI [BibTex]

mms Keskinbora, K. Ion beam lithographic and multilayer Fresnel zone plates for soft and hard X-rays: Nanofabrication and characterization Universität Stuttgart, Stuttgart, 2015 (phdthesis)

link (url) DOI [BibTex]

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

Tsatsoulis, T.

Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

mms

[BibTex]

mms Tsatsoulis, T. Transfer of angular momentum from the spin system to the lattice during ultrafast magnetization Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

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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]

mms Briones Paz, J. Z. Quantum kinetic theory of ultrafast demagnetization by electron-phonon scattering Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

[BibTex]

2009

Machine Learning for Brain-Computer Interfaces

Hill, NJ.

Mini-Symposia on Assistive Machine Learning for People with Disabilities at NIPS (AMD), December 2009 (talk)

Abstract

Brain-computer interfaces (BCI) aim to be the ultimate in assistive technology: decoding a user‘s intentions directly from brain signals without involving any muscles or peripheral nerves. Thus, some classes of BCI potentially offer hope for users with even the most extreme cases of paralysis, such as in late-stage Amyotrophic Lateral Sclerosis, where nothing else currently allows communication of any kind. Other lines in BCI research aim to restore lost motor function in as natural a way as possible, reconnecting and in some cases re-training motor-cortical areas to control prosthetic, or previously paretic, limbs. Research and development are progressing on both invasive and non-invasive fronts, although BCI has yet to make a breakthrough to widespread clinical application. The high-noise high-dimensional nature of brain-signals, particularly in non-invasive approaches and in patient populations, make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since "it doesn‘t matter what classifier you use once you‘ve done your preprocessing right and extracted the right features." I shall show a few examples of how this runs counter to both the empirical reality and the spirit of what needs to be done to bring BCI into clinical application. Along the way I‘ll highlight some of the interesting problems that remain open for machine-learners.

PDF Web Web [BibTex]

2009

ei Hill, NJ. Machine Learning for Brain-Computer Interfaces Mini-Symposia on Assistive Machine Learning for People with Disabilities at NIPS (AMD), December 2009 (talk)

PDF Web Web [BibTex]

PAC-Bayesian Approach to Formulation of Clustering Objectives

Seldin, Y.

NIPS Workshop on "Clustering: Science or Art? Towards Principled Approaches", December 2009 (talk)

Abstract

Clustering is a widely used tool for exploratory data analysis. However, the theoretical understanding of clustering is very limited. We still do not have a well-founded answer to the seemingly simple question of "how many clusters are present in the data?", and furthermore a formal comparison of clusterings based on different optimization objectives is far beyond our abilities. The lack of good theoretical support gives rise to multiple heuristics that confuse the practitioners and stall development of the field. We suggest that the ill-posed nature of clustering problems is caused by the fact that clustering is often taken out of its subsequent application context. We argue that one does not cluster the data just for the sake of clustering it, but rather to facilitate the solution of some higher level task. By evaluation of the clustering‘s contribution to the solution of the higher level task it is possible to compare different clusterings, even those obtained by different optimization objectives. In the preceding work it was shown that such an approach can be applied to evaluation and design of co-clustering solutions. Here we suggest that this approach can be extended to other settings, where clustering is applied.

PDF Web Web [BibTex]

ei Seldin, Y. PAC-Bayesian Approach to Formulation of Clustering Objectives NIPS Workshop on "Clustering: Science or Art? Towards Principled Approaches", December 2009 (talk)

PDF Web Web [BibTex]

Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data

Shelton, JA.

Women in Machine Learning Workshop (WiML), December 2009 (talk)

Abstract

Kernel Canonical Correlation Analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, KCCA learns representations tied more closely to underlying process generating the the data and can ignore high-variance noise directions. However, for data where acquisition in a given modality is expensive or otherwise limited, KCCA may suffer from small sample effects. We propose to use semi-supervised Laplacian regularization to utilize data that are present in only one modality. This manifold learning approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned and such data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single and multivariate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, Laplacian regularization improved performance whereas the semi-supervised variants of KCCA yielded the best performance. We additionally analyze the weights learned by the regression in order to infer brain regions that are important during different types of visual processing.

PDF Web [BibTex]

ei Shelton, JA. Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data Women in Machine Learning Workshop (WiML), December 2009 (talk)

PDF Web [BibTex]

Event-Related Potentials in Brain-Computer Interfacing

Hill, NJ.

Invited lecture on the bachelor & masters course "Introduction to Brain-Computer Interfacing", October 2009 (talk)

Abstract

An introduction to event-related potentials with specific reference to their use in brain-computer interfacing applications and research.

PDF [BibTex]

ei Hill, NJ. Event-Related Potentials in Brain-Computer Interfacing Invited lecture on the bachelor & masters course "Introduction to Brain-Computer Interfacing", October 2009 (talk)

PDF [BibTex]

BCI2000 and Python

Hill, NJ.

Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

Abstract

A tutorial, with exercises, on how to integrate your own Python code with the BCI2000 software package.

PDF [BibTex]

ei Hill, NJ. BCI2000 and Python Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

PDF [BibTex]

Implementing a Signal Processing Filter in BCI2000 Using C++

Hill, NJ., Mellinger, J.

Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

Abstract

This tutorial shows how the functionality of the BCI2000 software package can be extended with one‘s own code, using BCI2000‘s C++ API.

PDF [BibTex]

ei Hill, NJ., Mellinger, J. Implementing a Signal Processing Filter in BCI2000 Using C++ Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

PDF [BibTex]

Kernel Learning Approaches for Image Classification

Gehler, PV.

Biologische Kybernetik, Universität des Saarlandes, Saarbrücken, Germany, October 2009 (phdthesis)

Abstract

This thesis extends the use of kernel learning techniques to specific problems of image classification. Kernel learning is a paradigm in the field of machine learning that generalizes the use of inner products to compute similarities between arbitrary objects. In image classification one aims to separate images based on their visual content. We address two important problems that arise in this context: learning with weak label information and combination of heterogeneous data sources. The contributions we report on are not unique to image classification, and apply to a more general class of problems. We study the problem of learning with label ambiguity in the multiple instance learning framework. We discuss several different image classification scenarios that arise in this context and argue that the standard multiple instance learning requires a more detailed disambiguation. Finally we review kernel learning approaches proposed for this problem and derive a more efficient algorithm to solve them. The multiple kernel learning framework is an approach to automatically select kernel parameters. We extend it to its infinite limit and present an algorithm to solve the resulting problem. This result is then applied in two directions. We show how to learn kernels that adapt to the special structure of images. Finally we compare different ways of combining image features for object classification and present significant improvements compared to previous methods.

PDF [BibTex]

ei Gehler, PV. Kernel Learning Approaches for Image Classification Biologische Kybernetik, Universität des Saarlandes, Saarbrücken, Germany, October 2009 (phdthesis)

PDF [BibTex]

Learning Motor Primitives for Robotics

Kober, J., Peters, J., Oztop, E.

Advanced Telecommunications Research Center ATR, June 2009 (talk)

Abstract

The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.

[BibTex]

ei Kober, J., Peters, J., Oztop, E. Learning Motor Primitives for Robotics Advanced Telecommunications Research Center ATR, June 2009 (talk)

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Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer

Lampert, C.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2009 (talk)

Web [BibTex]

ei Lampert, C. Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2009 (talk)

Web [BibTex]

Kernel Methods in Computer Vision:Object Localization, Clustering,and Taxonomy Discovery

Blaschko, MB.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, March 2009 (phdthesis)

PDF PDF [BibTex]

ei Blaschko, MB. Kernel Methods in Computer Vision:Object Localization, Clustering,and Taxonomy Discovery Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, March 2009 (phdthesis)

PDF PDF [BibTex]

Motor Control and Learning in Table Tennis

Mülling, K.

Eberhard Karls Universität Tübingen, Gerrmany, 2009 (diplomathesis)

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ei Mülling, K. Motor Control and Learning in Table Tennis Eberhard Karls Universität Tübingen, Gerrmany, 2009 (diplomathesis)

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Hierarchical Clustering and Density Estimation Based on k-nearest-neighbor graphs

Drewe, P.

Eberhard Karls Universität Tübingen, Germany, 2009 (diplomathesis)

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ei Drewe, P. Hierarchical Clustering and Density Estimation Based on k-nearest-neighbor graphs Eberhard Karls Universität Tübingen, Germany, 2009 (diplomathesis)

[BibTex]

Learning with Structured Data: Applications to Computer Vision

Nowozin, S.

Technische Universität Berlin, Germany, 2009 (phdthesis)

PDF [BibTex]

ei Nowozin, S. Learning with Structured Data: Applications to Computer Vision Technische Universität Berlin, Germany, 2009 (phdthesis)

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From Differential Equations to Differential Geometry: Aspects of Regularisation in Machine Learning

Steinke, F.

Universität des Saarlandes, Saarbrücken, Germany, 2009 (phdthesis)

PDF [BibTex]

ei Steinke, F. From Differential Equations to Differential Geometry: Aspects of Regularisation in Machine Learning Universität des Saarlandes, Saarbrücken, Germany, 2009 (phdthesis)

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Magnetische L10-FePt Nanostrukturen für höchste Datenspeicherdichten

Breitling, A.

Universität Stuttgart, Stuttgart, 2009 (phdthesis)

mms

[BibTex]

mms Breitling, A. Magnetische L10-FePt Nanostrukturen für höchste Datenspeicherdichten Universität Stuttgart, Stuttgart, 2009 (phdthesis)

[BibTex]

Ab-initio Elliott-Yafet modeling of ultrafast demagnetization after laser irradiation

Illg, C.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

mms Illg, C. Ab-initio Elliott-Yafet modeling of ultrafast demagnetization after laser irradiation Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

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Element specific investigation of the magnetization profile at the CrO2/RuO2 interface

Zafar, K.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

mms Zafar, K. Element specific investigation of the magnetization profile at the CrO2/RuO2 interface Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

[BibTex]

Bayesian Methods for Autonomous Learning Systems (Phd Thesis)

Ting, J.

Department of Computer Science, University of Southern California, Los Angeles, CA, 2009, clmc (phdthesis)

PDF [BibTex]

am Ting, J. Bayesian Methods for Autonomous Learning Systems (Phd Thesis) Department of Computer Science, University of Southern California, Los Angeles, CA, 2009, clmc (phdthesis)

PDF [BibTex]

Magnetic resonant reflectometry on exchange bias systems

Brück, S.

Universität Stuttgart, Stuttgart, 2009 (phdthesis)

mms

link (url) [BibTex]

mms Brück, S. Magnetic resonant reflectometry on exchange bias systems Universität Stuttgart, Stuttgart, 2009 (phdthesis)

link (url) [BibTex]

In-situ - Untersuchungen zu Interdiffusion und Magnetismus in magnetischen Multilayern

Schmidt, M.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

mms Schmidt, M. In-situ - Untersuchungen zu Interdiffusion und Magnetismus in magnetischen Multilayern Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

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Theorie der elektronischen Zustände in oxidischen magnetischen Materialien

Kostoglou, C.

Universität Stuttgart, Stuttgart, 2009 (phdthesis)

mms

[BibTex]

mms Kostoglou, C. Theorie der elektronischen Zustände in oxidischen magnetischen Materialien Universität Stuttgart, Stuttgart, 2009 (phdthesis)

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Magnetooptische Untersuchungen an Ferromagnet- und Supraleiter-Nanosystemen und deren Hybriden

Treiber, S.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

mms Treiber, S. Magnetooptische Untersuchungen an Ferromagnet- und Supraleiter-Nanosystemen und deren Hybriden Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

[BibTex]

2004

How to learn from very few examples?

Zhou, D.

October 2004 (talk)

PDF [BibTex]

2004

ei Zhou, D. How to learn from very few examples? October 2004 (talk)

PDF [BibTex]

Discrete vs. Continuous: Two Sides of Machine Learning

Zhou, D.

October 2004 (talk)

PDF [BibTex]

ei Zhou, D. Discrete vs. Continuous: Two Sides of Machine Learning October 2004 (talk)

PDF [BibTex]

Discrete vs. Continuous: Two Sides of Machine Learning

Zhou, D.

October 2004 (talk)

Abstract

We consider the problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data. This talk is mainly based on the followiing contribution: (1) D. Zhou and B. Sch{\"o}lkopf: Transductive Inference with Graphs, MPI Technical report, August, 2004; (2) D. Zhou, B. Sch{\"o}lkopf and T. Hofmann. Semi-supervised Learning on Directed Graphs. NIPS 2004; (3) D. Zhou, O. Bousquet, T.N. Lal, J. Weston and B. Sch{\"o}lkopf. Learning with Local and Global Consistency. NIPS 2003.

PDF [BibTex]

ei Zhou, D. Discrete vs. Continuous: Two Sides of Machine Learning October 2004 (talk)

PDF [BibTex]

Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung

Eichhorn, J.

September 2004 (talk)

Abstract

Invited talk at the workshop "Numerical, Statistical and Discrete Methods in Image Processing" at the TU M{\"u}nchen (in GERMAN)

PDF [BibTex]

ei Eichhorn, J. Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung September 2004 (talk)

PDF [BibTex]

Riemannian Geometry on Graphs and its Application to Ranking and Classification

Zhou, D.

June 2004 (talk)

Abstract

PDF [BibTex]

ei Zhou, D. Riemannian Geometry on Graphs and its Application to Ranking and Classification June 2004 (talk)

PDF [BibTex]

Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking

Zhou, D.

January 2004 (talk)

Abstract

We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

PDF [BibTex]

ei Zhou, D. Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking January 2004 (talk)

PDF [BibTex]

Introduction to Category Theory

Bousquet, O.

Internal Seminar, January 2004 (talk)

Abstract

A brief introduction to the general idea behind category theory with some basic definitions and examples. A perspective on higher dimensional categories is given.

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ei Bousquet, O. Introduction to Category Theory Internal Seminar, January 2004 (talk)

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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)

PDF PostScript [BibTex]

ei von Luxburg, U. Statistical Learning with Similarity and Dissimilarity Functions pages: 1-166, Technische Universität Berlin, Germany, Technische Universität Berlin, Germany, 2004 (phdthesis)

PDF PostScript [BibTex]

Classification and Feature Extraction in Man and Machine

Graf, AAB.

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

[BibTex]

ei Graf, AAB. Classification and Feature Extraction in Man and Machine Biologische Kybernetik, University of Tübingen, Germany, 2004, online publication (phdthesis)

[BibTex]

Advanced Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, 2004 (talk)

PDF [BibTex]

ei Bousquet, O. Advanced Statistical Learning Theory Machine Learning Summer School, 2004 (talk)

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Investigation of oxide layers in tunnel junctions

Amaladass, E. P.

University of Stuttgart, Stuttgart, 2004 (mastersthesis)

mms

[BibTex]

mms Amaladass, E. P. Investigation of oxide layers in tunnel junctions University of Stuttgart, Stuttgart, 2004 (mastersthesis)

[BibTex]

Untersuchung der Desorptionskinetik von Metallhydriden in Bezug auf technische Anwendungen

von Zeppelin, F.

Universität Stuttgart, Stuttgart, 2004 (phdthesis)

mms

[BibTex]

mms von Zeppelin, F. Untersuchung der Desorptionskinetik von Metallhydriden in Bezug auf technische Anwendungen Universität Stuttgart, Stuttgart, 2004 (phdthesis)

[BibTex]

Towards Tractable Parameter-Free Statistical Learning (Phd Thesis)

D’Souza, A

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

link (url) [BibTex]

am D’Souza, A Towards Tractable Parameter-Free Statistical Learning (Phd Thesis) Department of Computer Science, University of Southern California, Los Angeles, 2004, clmc (phdthesis)

link (url) [BibTex]