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30 results (View BibTeX file of all listed publications)

2016

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Interface-controlled phenomena in nanomaterials

Mittemeijer, Eric J.; Wang, Zumin

2016 (mpi_year_book)

Abstract
Nanosized material systems characteristically exhibit an excessively high internal interface density. A series of previously unknown phenomena in nanomaterials have been disclosed that are fundamentally caused by the presence of interfaces. Thus anomalously large and small lattice parameters in nanocrystalline metals, quantum stress oscillations in growing nanofilms, and extraordinary atomic mobility at ultralow temperatures have been observed and explained. The attained understanding for these new phenomena can lead to new, sophisticated applications of nanomaterials in advanced technologies.

link (url) [BibTex]

2016

Mittemeijer, Eric J.; Wang, Zumin Interface-controlled phenomena in nanomaterials 2016 (mpi_year_book)

link (url) [BibTex]

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Robots learn how to see

Geiger, A.

2016 (mpi_year_book)

Abstract
Autonomous vehicles and intelligent service robots could soon contribute to making our lives more pleasant and secure. However, for autonomous operation such systems first need to learn the perception process itself. This involves measuring distances and motions, detecting objects and interpreting the threedimensional world as a whole. While humans perceive their environment with seemingly little efforts, computers first need to be trained for these tasks. Our research is concerned with developing mathematical models which allow computers to robustly perceive their environment.

link (url) DOI [BibTex]

2015

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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]

2015

ei Besserve, M. Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism 53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

[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]

ei Besserve, M. Independence of cause and mechanism in brain networks DALI workshop on Networks: Processes and Causality, April 2015 (talk)

[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]

ei Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D. Information-Theoretic Implications of Classical and Quantum Causal Structures 18th Conference on Quantum Information Processing (QIP), 2015 (talk)

Web link (url) [BibTex]

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Learning robots

Trimpe, S.

2015 (mpi_year_book)

Abstract
An exploded power plant, collapsed buildings after an earthquake, a burning vehicle loaded with hazardous goods – all of these are dangerous situations for human emergency responders. What if we could send robots instead of humans? Researchers at the Autonomous Motion Department work on fundamental principles required to build intelligent robots which one day can help us in dangerous situations. A key requirement for making this happen is that robots must be enabled to learn.

link (url) [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]

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]

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The smallest human-made nano-motor

Sánchez, Samuel

2015 (mpi_year_book)

Abstract
Tiny self-propelled motors which speed through the water and clean up pollutions along the way or small robots which can swim effortlessly through blood to one day transport medication to a certain part of the body – this sounds like taken from a science fiction movie script. However, Samuel Sánchez is already hard at work in his lab at the Max Planck Institute for Intelligent Systems in Stuttgart to make these visions come true. Self-propelled micro-nanorobots and the usage as integrated sensors in microfluid-chips: that’s the topic of Sánchez` research group.

link (url) [BibTex]

Sánchez, Samuel The smallest human-made nano-motor 2015 (mpi_year_book)

link (url) [BibTex]

2005

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Some thoughts about Gaussian Processes

Chapelle, O.

NIPS Workshop on Open Problems in Gaussian Processes for Machine Learning, December 2005 (talk)

ei

PDF Web [BibTex]

2005

ei Chapelle, O. Some thoughts about Gaussian Processes NIPS Workshop on Open Problems in Gaussian Processes for Machine Learning, December 2005 (talk)

PDF Web [BibTex]

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Building Sparse Large Margin Classifiers

Wu, M., Schölkopf, B., BakIr, G.

The 22nd International Conference on Machine Learning (ICML), August 2005 (talk)

ei

PDF [BibTex]

ei Wu, M., Schölkopf, B., BakIr, G. Building Sparse Large Margin Classifiers The 22nd International Conference on Machine Learning (ICML), August 2005 (talk)

PDF [BibTex]

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Learning from Labeled and Unlabeled Data on a Directed Graph

Zhou, D.

The 22nd International Conference on Machine Learning, August 2005 (talk)

Abstract
We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The time complexity of the algorithm derived from this framework is nearly linear due to recently developed numerical techniques. In the absence of labeled instances, this framework can be utilized as a spectral clustering method for directed graphs, which generalizes the spectral clustering approach for undirected graphs. We have applied our framework to real-world web classification problems and obtained encouraging results.

ei

PDF [BibTex]

ei Zhou, D. Learning from Labeled and Unlabeled Data on a Directed Graph The 22nd International Conference on Machine Learning, August 2005 (talk)

PDF [BibTex]

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Machine-Learning Approaches to BCI in Tübingen

Bensch, M., Bogdan, M., Hill, N., Lal, T., Rosenstiel, W., Schölkopf, B., Schröder, M.

Brain-Computer Interface Technology, June 2005, Talk given by NJH. (talk)

ei

[BibTex]

ei Bensch, M., Bogdan, M., Hill, N., Lal, T., Rosenstiel, W., Schölkopf, B., Schröder, M. Machine-Learning Approaches to BCI in Tübingen Brain-Computer Interface Technology, June 2005, Talk given by NJH. (talk)

[BibTex]

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Kernel Constrained Covariance for Dependence Measurement

Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., Murayama, Y., Schölkopf, B., Logothetis, N.

AISTATS, January 2005 (talk)

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. All current kernel-based independence tests share this behaviour. We demonstrate exponential convergence between the population and empirical COCO. Finally, we use COCO as a measure of joint neural activity between voxels in MRI recordings of the macaque monkey, and compare the results to the mutual information and the correlation. We also show the effect of removing breathing artefacts from the MRI recording.

ei

PostScript [BibTex]

ei Gretton, A., Smola, A., Bousquet, O., Herbrich, R., Belitski, A., Augath, M., Murayama, Y., Schölkopf, B., Logothetis, N. Kernel Constrained Covariance for Dependence Measurement AISTATS, January 2005 (talk)

PostScript [BibTex]

2004

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How to learn from very few examples?

Zhou, D.

October 2004 (talk)

ei

PDF [BibTex]

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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.

ei

PDF [BibTex]

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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)

ei

PDF [BibTex]

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Kernel Methods in Computational Biology

Schölkopf, B., Tsuda, K., Vert, J.

pages: 410, Computational Molecular Biology, MIT Press, Cambridge, MA, USA, August 2004 (book)

Abstract
Modern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DNA or amino acid sequences), and the need to combine heterogeneous sources of information. This book provides a detailed overview of current research in kernel methods and their applications to computational biology. Following three introductory chapters—an introduction to molecular and computational biology, a short review of kernel methods that focuses on intuitive concepts rather than technical details, and a detailed survey of recent applications of kernel methods in computational biology—the book is divided into three sections that reflect three general trends in current research. The first part presents different ideas for the design of kernel functions specifically adapted to various biological data; the second part covers different approaches to learning from heterogeneous data; and the third part offers examples of successful applications of support vector machine methods.

ei

Web [BibTex]

ei Schölkopf, B., Tsuda, K., Vert, J. Kernel Methods in Computational Biology pages: 410, Computational Molecular Biology, MIT Press, Cambridge, MA, USA, August 2004 (book)

Web [BibTex]

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Riemannian Geometry on Graphs and its Application to Ranking and Classification

Zhou, D.

June 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.

ei

PDF [BibTex]

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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.

ei

PDF [BibTex]

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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.

ei

PDF [BibTex]

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

PDF [BibTex]

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Advanced Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, 2004 (talk)

ei

PDF [BibTex]

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

PDF [BibTex]

2003

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Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

ei

PDF [BibTex]

2003

ei Bousquet, O. Statistical Learning Theory Machine Learning Summer School, August 2003 (talk)

PDF [BibTex]

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Remarks on Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

ei

PDF [BibTex]

ei Bousquet, O. Remarks on Statistical Learning Theory Machine Learning Summer School, August 2003 (talk)

PDF [BibTex]

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Rademacher and Gaussian averages in Learning Theory

Bousquet, O.

Universite de Marne-la-Vallee, March 2003 (talk)

ei

PDF [BibTex]

ei Bousquet, O. Rademacher and Gaussian averages in Learning Theory Universite de Marne-la-Vallee, March 2003 (talk)

PDF [BibTex]

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Introduction: Robots with Cognition?

Franz, MO.

6, pages: 38, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (talk)

Abstract
Using robots as models of cognitive behaviour has a long tradition in robotics. Parallel to the historical development in cognitive science, one observes two major, subsequent waves in cognitive robotics. The first is based on ideas of classical, cognitivist Artificial Intelligence (AI). According to the AI view of cognition as rule-based symbol manipulation, these robots typically try to extract symbolic descriptions of the environment from their sensors that are used to update a common, global world representation from which, in turn, the next action of the robot is derived. The AI approach has been successful in strongly restricted and controlled environments requiring well-defined tasks, e.g. in industrial assembly lines. AI-based robots mostly failed, however, in the unpredictable and unstructured environments that have to be faced by mobile robots. This has provoked the second wave in cognitive robotics which tries to achieve cognitive behaviour as an emergent property from the interaction of simple, low-level modules. Robots of the second wave are called animats as their architecture is designed to closely model aspects of real animals. Using only simple reactive mechanisms and Hebbian-type or evolutionary learning, the resulting animats often outperformed the highly complex AI-based robots in tasks such as obstacle avoidance, corridor following etc. While successful in generating robust, insect-like behaviour, typical animats are limited to stereotyped, fixed stimulus-response associations. If one adopts the view that cognition requires a flexible, goal-dependent choice of behaviours and planning capabilities (H.A. Mallot, Kognitionswissenschaft, 1999, 40-48) then it appears that cognitive behaviour cannot emerge from a collection of purely reactive modules. It rather requires environmentally decoupled structures that work without directly engaging the actions that it is concerned with. This poses the current challenge to cognitive robotics: How can we build cognitive robots that show the robustness and the learning capabilities of animats without falling back into the representational paradigm of AI? The speakers of the symposium present their approaches to this question in the context of robot navigation and sensorimotor learning. In the first talk, Prof. Helge Ritter introduces a robot system for imitation learning capable of exploring various alternatives in simulation before actually performing a task. The second speaker, Angelo Arleo, develops a model of spatial memory in rat navigation based on his electrophysiological experiments. He validates the model on a mobile robot which, in some navigation tasks, shows a performance comparable to that of the real rat. A similar model of spatial memory is used to investigate the mechanisms of territory formation in a series of robot experiments presented by Prof. Hanspeter Mallot. In the last talk, we return to the domain of sensorimotor learning where Ralf M{\"o}ller introduces his approach to generate anticipatory behaviour by learning forward models of sensorimotor relationships.

ei

Web [BibTex]

ei Franz, MO. Introduction: Robots with Cognition? 6, pages: 38, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot, R. Ulrich, F.A. Wichmann), 6. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2003 (talk)

Web [BibTex]

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Magnetism and the Microstructure of Ferromagnetic Solids

Kronmüller, H., Fähnle, M.

pages: 432 p., 1st ed., Cambridge University Press, Cambridge, 2003 (book)

mms

[BibTex]

mms Kronmüller, H., Fähnle, M. Magnetism and the Microstructure of Ferromagnetic Solids pages: 432 p., 1st ed., Cambridge University Press, Cambridge, 2003 (book)

[BibTex]