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2013


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The avalanche process in gold covered MgB2 films

Stahl, C., Treiber, S., Schütz, G., Albrecht, J.

{Superconductor Science and Technology}, 26, IOP Pub., Bristol, 2013 (article)

mms

DOI [BibTex]

2013


DOI [BibTex]


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MFU-4 - A metal-organic framework for highly effective H2/D2 separation

Teufel, J., Oh, H., Hirscher, M., Wahiduzzaman, M., Zhechkov, L., Kuc, A., Heine, T., Denysenko, D., Volkmer, D.

{Advanced Materials}, 25(4):635-639, Wiley-VCH Verlag, Weinheim, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Delayed magnetic vortex core reversal

Kammerer, M., Sproll, M., Stoll, H., Noske, M., Weigand, M., Illg, C., Fähnle, M., Schütz, G.

{Applied Physics Letters}, 102, American Institute of Physics, Melville, NY, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Self-organized state formation in magnonic vortex crystals

Adolff, C. F., Hänze, M., Vogel, A., Weigand, M., Martens, M., Meier, G.

{Physical Review B}, 88(22), American Physical Society, Woodbury, NY, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Erratum: Generalized Gilbert equation including inertial damping: Derivation from an extended breathing Fermi surface model [Phys. Rev. B 84, 172403 (2011)]

Fähnle, M., Steiauf, D., Illg, C.

{Physical Review B}, 88, American Physical Society, Woodbury, NY, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Strain and composition dependence of orbital polarization in nickel oxide superlattices

Wu, M., Benckiser, E., Haverkort, M. W., Franco, A., Lu, J., Nwankwo, U., Brück, S., Audehm, P., Goering, E., Macke, S., Hinkov, V., Wochner, P., Christiani, G., Heinze, S., Logvenov, G., Habermeier, H., Keimer, B.

{Physical Review B}, 88, Published by the American Physical Society through the American Institute of Physics, Woodbury, NY, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Efficient focusing of 8 keV X-rays with multilayer Fresnel zone plates fabricated by atomic layer deposition and focused ion beam milling

Mayer, M., Keskinbora, K., Grévent, C., Szeghalmi, A., Knez, M., Weigand, M., Snigirev, A., Snigereva, I., Schütz, G.

{Journal of Synchrotron Radiation}, 20, pages: 433-440, Published for the International Union of Crystallography by Munksgaard, Copenhagen, Denmark, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Rapid prototyping of Fresnel zone plates via direct Ga+ ion beam lithography for high-resolution x-ray imaging

Keskinbora, K., Grévent, C., Eigenthaler, U., Weigand, M., Schütz, G.

{ACS Nano}, 7(11):9788-9797, American Chemical Society, Washington, DC, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Eine kryoflexible kovalente organische Gerüststruktur für die effiziente Trennung von Wasserstoffisotopien durch Quantensieben

Oh, H., Kalidindi, S. B., Um, Y., Bureekaew, S., Schmid, R., Fischer, R. A., Hirscher, M.

{Angewandte Chemie}, 125(50):13461-13464, Wiley-VCH Verl., Weinheim, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Ultrafast demagnetization after laser irradiation in transition metals: Ab initio calculations of the spin-flip electron-phonon scattering with reduced exchange splitting

Illg, C., Haag, M., Fähnle, M.

{Physical Review B}, 88, American Physical Society, Woodbury, NY, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Phase diagram for magnetic vortex core switching studied by ferromagnetic absorption spectroscopy and time-resolved transmission x-ray microscopy

Martens, M., Kamionka, T., Weigand, M., Stoll, H., Tyliszczak, T., Meier, G.

{Physical Review B}, 87, Published by the American Physical Society through the American Institute of Physics, Woodbury, NY, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Thermodynamics as a theory of decision-making with information-processing costs

Ortega, PA, Braun, DA

Proceedings of the Royal Society of London A, 469(2153):1-18, May 2013 (article)

Abstract
Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here, we propose a thermodynamically inspired formalization of bounded rational decision-making where information processing is modelled as state changes in thermodynamic systems that can be quantified by differences in free energy. By optimizing a free energy, bounded rational decision-makers trade off expected utility gains and information-processing costs measured by the relative entropy. As a result, the bounded rational decision-making problem can be rephrased in terms of well-known variational principles from statistical physics. In the limit when computational costs are ignored, the maximum expected utility principle is recovered. We discuss links to existing decision-making frameworks and applications to human decision-making experiments that are at odds with expected utility theory. Since most of the mathematical machinery can be borrowed from statistical physics, the main contribution is to re-interpret the formalism of thermodynamic free-energy differences in terms of bounded rational decision-making and to discuss its relationship to human decision-making experiments.

ei

DOI [BibTex]

DOI [BibTex]


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Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis

Zahedi, K., Martius, G., Ay, N.

Frontiers in Psychology, 4(801), 2013 (article)

Abstract
One of the main challenges in the field of embodied artificial intelligence is the open-ended autonomous learning of complex behaviours. Our approach is to use task-independent, information-driven intrinsic motivation(s) to support task-dependent learning. The work presented here is a preliminary step in which we investigate the predictive information (the mutual information of the past and future of the sensor stream) as an intrinsic drive, ideally supporting any kind of task acquisition. Previous experiments have shown that the predictive information (PI) is a good candidate to support autonomous, open-ended learning of complex behaviours, because a maximisation of the PI corresponds to an exploration of morphology- and environment-dependent behavioural regularities. The idea is that these regularities can then be exploited in order to solve any given task. Three different experiments are presented and their results lead to the conclusion that the linear combination of the one-step PI with an external reward function is not generally recommended in an episodic policy gradient setting. Only for hard tasks a great speed-up can be achieved at the cost of an asymptotic performance lost.

al

link (url) DOI [BibTex]


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Switching modes in easy and hard axis magnetic reversal in a self-assembled antidot array

Haering, F., Wiedwald, U., Nothelfer, S., Koslowski, B., Ziemann, P., Lechner, L., Wallucks, A., Lebecki, K., Nowak, U., Gräfe, J., Goering, E., Schütz, G.

{Nanotechnology}, 24, IOP Pub., Bristol, UK, 2013 (article)

mms

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Time-resolved imaging of nonlinear magnetic domain-wall dynamics in ferromagnetic nanowires

Stein, F.-U., Bocklage, L., Weigand, M., Meier, G.

{Scientific Reports}, 3, Nature Publishing Group, London, UK, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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A cryogenically flexible covalent organic framework for efficient hydrogen isotrope separation by quantum sieving

Oh, H., Kalidindi, S. B., Um, Y., Bureekaew, S., Schmid, R., Fischer, R. A., Hirscher, M.

{Angewandte Chemie International Edition in English}, 52(50):13219-13222, Wiley-VCH Verlag GmbH & Co. KGaA, D-69451 Weinheim, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Unexpected room-temperature ferromagnetism in bulk ZnO

Chen, Y., Goering, E., Jeurgens, L., Wang, Z., Phillipp, F., Baier, J., Tietze, T., Schütz, G.

{Applied Physics Letters}, (103), American Institute of Physics, Melville, NY, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Large-area hard magnetic L10-FePt and composite L10-FePt based nanopatterns

Goll, D., Bublat, T.

{Physica Status Solidi A-Applications and Materials Science}, 210(7):1261-1271, Wiley-VCH, Weinheim, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Wave modes of collective vortex gyration in dipolar-coupled-dot-array magnonic crystals

Han, D., Vogel, A., Jung, H., Lee, K., Weigand, M., Stoll, H., Schütz, G., Fischer, P., Meier, G., Kim, S.

{Scientific Reports}, 3, Nature Publishing Group, London, UK, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Abstraction in Decision-Makers with Limited Information Processing Capabilities

Genewein, T, Braun, DA

pages: 1-9, NIPS Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013 (conference)

Abstract
A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise. From an information theoretic point of view abstractions are desirable because they allow for very efficient information processing. In artificial systems abstractions are often implemented through computationally costly formations of groups or clusters. In this work we establish the relation between the free-energy framework for decision-making and rate-distortion theory and demonstrate how the application of rate-distortion for decision-making leads to the emergence of abstractions. We argue that abstractions are induced due to a limit in information processing capacity.

ei

link (url) [BibTex]

link (url) [BibTex]


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Robustness of guided self-organization against sensorimotor disruptions

Martius, G.

Advances in Complex Systems, 16(02n03):1350001, 2013 (article)

Abstract
Self-organizing processes are crucial for the development of living beings. Practical applications in robots may benefit from the self-organization of behavior, e.g.~to increase fault tolerance and enhance flexibility, provided that external goals can also be achieved. We present results on the guidance of self-organizing control by visual target stimuli and show a remarkable robustness to sensorimotor disruptions. In a proof of concept study an autonomous wheeled robot is learning an object finding and ball-pushing task from scratch within a few minutes in continuous domains. The robustness is demonstrated by the rapid recovery of the performance after severe changes of the sensor configuration.

al

DOI [BibTex]

DOI [BibTex]


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Bounded Rational Decision-Making in Changing Environments

Grau-Moya, J, Braun, DA

pages: 1-9, NIPS Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013 (conference)

Abstract
A perfectly rational decision-maker chooses the best action with the highest utility gain from a set of possible actions. The optimality principles that describe such decision processes do not take into account the computational costs of finding the optimal action. Bounded rational decision-making addresses this problem by specifically trading off information-processing costs and expected utility. Interestingly, a similar trade-off between energy and entropy arises when describing changes in thermodynamic systems. This similarity has been recently used to describe bounded rational agents. Crucially, this framework assumes that the environment does not change while the decision-maker is computing the optimal policy. When this requirement is not fulfilled, the decision-maker will suffer inefficiencies in utility, that arise because the current policy is optimal for an environment in the past. Here we borrow concepts from non-equilibrium thermodynamics to quantify these inefficiencies and illustrate with simulations its relationship with computational resources.

ei

link (url) [BibTex]

link (url) [BibTex]


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Ferromagnetism of zinc oxide nanograined films

Straumal, B. B., Protasova, S. G., Mazilkin, A. A., Schütz, G., Goering, E., Baretzky, B., Straumal, P. B.

{Journal of Experimental and Theoretical Physics Letters}, 97(6):367-377, Pleiades Publishing, Inc., 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Hydrogen adsorption properties of platinum decorated hierarchically structured templated carbons

Oh, H., Gennett, T., Atanassov, P., Kurttepeli, M., Bals, S., Hurst, K. E., Hirscher, M.

{Microporous and Mesoporous Materials}, pages: 66-74, Elsevier, Amsterdam, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Extended s-d models for the dynamics of noncollinear magnetization: Short review of two different approaches

Fähnle, M., Zhang, S.

{Journal of Magnetism and Magnetic Materials}, 326, pages: 232-234, NH, Elsevier, Amsterdam, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Correlation between spin structure oscillations and domain wall velocities

Bisig, A., Stärk, M., Mawass, M., Moutafis, C., Rhensius, J., Heidler, J., Büttner, F., Noske, M., Weigand, M., Eisebitt, S., Tyliszczak, T., Van Wayenberge, B., Stoll, H., Schütz, G., Kläui, M.

{Nature Communications}, 4, Nature Publishing Group, London, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Recent advances in use of atomic layer deposition and focused ion beams for fabrication of Fresnel zone plates for hard x-rays

Keskinbora, K., Robisch, A., Mayer, M., Grévent, C., Szeghalmi, A. V., Knez, M., Weigand, M., Snigireva, I., Snigirev, A., Salditt, T., Schütz, G.

{Proceedings of SPIE (The International Society for Optical Engineering)}, 8851, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Magnetic states in low-pinning high-anisotropy material nanostructures suitable for dynamic imaging

Büttner, F., Moutafis, C., Bisig, A., Wohlhüter, P., Günther, C. M., Mohanty, J., Geilhufe, J., Schneider, M., v. Korff Schmising, C., Schaffert, S., Pfau, B., Hantschmann, M., Riemeier, M., Emmel, M., Finizio, S., Jakob, G., Weigand, M., Rhensius, J., Franken, J. H., Lavrijsen, R., Swagten, H. J. M., Stoll, H., Eisebitt, S., Kläui, M.

{Physical Review B}, 87, Published by the American Physical Society through the American Institute of Physics, Woodbury, NY, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Experimental and theoretical study of D2/H2 quantum sieving in a carbon molecular sieve

Gotzias, A., Charalambopoulou, G., Ampoumogli, A., Krkljus, I., Hirscher, M., Steriotis, T.

{Adsorption}, 19(2-4):373-379, Springer Science+Business Media, New York, 2013 (article)

mms

DOI [BibTex]

DOI [BibTex]

2003


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Concentration Inequalities for Sub-Additive Functions Using the Entropy Method

Bousquet, O.

Stochastic Inequalities and Applications, 56, pages: 213-247, Progress in Probability, (Editors: Giné, E., C. Houdré and D. Nualart), November 2003 (article)

Abstract
We obtain exponential concentration inequalities for sub-additive functions of independent random variables under weak conditions on the increments of those functions, like the existence of exponential moments for these increments. As a consequence of these general inequalities, we obtain refinements of Talagrand's inequality for empirical processes and new bounds for randomized empirical processes. These results are obtained by further developing the entropy method introduced by Ledoux.

ei

PostScript [BibTex]

2003


PostScript [BibTex]


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On the Complexity of Learning the Kernel Matrix

Bousquet, O., Herrmann, D.

In Advances in Neural Information Processing Systems 15, pages: 399-406, (Editors: Becker, S. , S. Thrun, K. Obermayer), The MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We investigate data based procedures for selecting the kernel when learning with Support Vector Machines. We provide generalization error bounds by estimating the Rademacher complexities of the corresponding function classes. In particular we obtain a complexity bound for function classes induced by kernels with given eigenvectors, i.e., we allow to vary the spectrum and keep the eigenvectors fix. This bound is only a logarithmic factor bigger than the complexity of the function class induced by a single kernel. However, optimizing the margin over such classes leads to overfitting. We thus propose a suitable way of constraining the class. We use an efficient algorithm to solve the resulting optimization problem, present preliminary experimental results, and compare them to an alignment-based approach.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Cluster Kernels for Semi-Supervised Learning

Chapelle, O., Weston, J., Schölkopf, B.

In Advances in Neural Information Processing Systems 15, pages: 585-592, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We propose a framework to incorporate unlabeled data in kernel classifier, based on the idea that two points in the same cluster are more likely to have the same label. This is achieved by modifying the eigenspectrum of the kernel matrix. Experimental results assess the validity of this approach.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Mismatch String Kernels for SVM Protein Classification

Leslie, C., Eskin, E., Weston, J., Noble, W.

In Advances in Neural Information Processing Systems 15, pages: 1417-1424, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure sequence similarity based on shared occurrences of k-length subsequences, counted with up to m mismatches, and do not rely on any generative model for the positive training sequences. We compute the kernels efficiently using a mismatch tree data structure and report experiments on a benchmark SCOP dataset, where we show that the mismatch kernel used with an SVM classifier performs as well as the Fisher kernel, the most successful method for remote homology detection, while achieving considerable computational savings.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel Dependency Estimation

Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., Vapnik, V.

In Advances in Neural Information Processing Systems 15, pages: 873-880, (Editors: S Becker and S Thrun and K Obermayer), MIT Press, Cambridge, MA, USA, 16th Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model

Franz, MO., Chahl, JS.

In Advances in Neural Information Processing Systems 15, pages: 1319-1326, (Editors: Becker, S., S. Thrun and K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates turn out to be less reliable.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Clustering with the Fisher score

Tsuda, K., Kawanabe, M., Müller, K.

In Advances in Neural Information Processing Systems 15, pages: 729-736, (Editors: Becker, S. , S. Thrun, K. Obermayer), MIT Press, Cambridge, MA, USA, Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), October 2003 (inproceedings)

Abstract
Recently the Fisher score (or the Fisher kernel) is increasingly used as a feature extractor for classification problems. The Fisher score is a vector of parameter derivatives of loglikelihood of a probabilistic model. This paper gives a theoretical analysis about how class information is preserved in the space of the Fisher score, which turns out that the Fisher score consists of a few important dimensions with class information and many nuisance dimensions. When we perform clustering with the Fisher score, K-Means type methods are obviously inappropriate because they make use of all dimensions. So we will develop a novel but simple clustering algorithm specialized for the Fisher score, which can exploit important dimensions. This algorithm is successfully tested in experiments with artificial data and real data (amino acid sequences).

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Marginalized Kernels between Labeled Graphs

Kashima, H., Tsuda, K., Inokuchi, A.

In 20th International Conference on Machine Learning, pages: 321-328, (Editors: Faucett, T. and N. Mishra), 20th International Conference on Machine Learning, August 2003 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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Sparse Gaussian Processes: inference, subspace identification and model selection

Csato, L., Opper, M.

In Proceedings, pages: 1-6, (Editors: Van der Hof, , Wahlberg), The Netherlands, 13th IFAC Symposium on System Identifiaction, August 2003, electronical version; Index ThA02-2 (inproceedings)

Abstract
Gaussian Process (GP) inference is a probabilistic kernel method where the GP is treated as a latent function. The inference is carried out using the Bayesian online learning and its extension to the more general iterative approach which we call TAP/EP learning. Sparsity is introduced in this context to make the TAP/EP method applicable to large datasets. We address the prohibitive scaling of the number of parameters by defining a subset of the training data that is used as the support the GP, thus the number of required parameters is independent of the training set, similar to the case of ``Support--‘‘ or ``Relevance--Vectors‘‘. An advantage of the full probabilistic treatment is that allows the computation of the marginal data likelihood or evidence, leading to hyper-parameter estimation within the GP inference. An EM algorithm to choose the hyper-parameters is proposed. The TAP/EP learning is the E-step and the M-step then updates the hyper-parameters. Due to the sparse E-step the resulting algorithm does not involve manipulation of large matrices. The presented algorithm is applicable to a wide variety of likelihood functions. We present results of applying the algorithm on classification and nonstandard regression problems for artificial and real datasets.

ei

PDF GZIP [BibTex]

PDF GZIP [BibTex]


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Adaptive, Cautious, Predictive control with Gaussian Process Priors

Murray-Smith, R., Sbarbaro, D., Rasmussen, CE., Girard, A.

In Proceedings of the 13th IFAC Symposium on System Identification, pages: 1195-1200, (Editors: Van den Hof, P., B. Wahlberg and S. Weiland), Proceedings of the 13th IFAC Symposium on System Identification, August 2003 (inproceedings)

Abstract
Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example.

ei

PDF [BibTex]

PDF [BibTex]


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Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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Remarks on Statistical Learning Theory

Bousquet, O.

Machine Learning Summer School, August 2003 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics

Ilg, W., Bakir, GH., Mezger, J., Giese, MA.

In Humanoids Proceedings, pages: 0-0, Humanoids Proceedings, July 2003, electronical version (inproceedings)

Abstract
In this paper we present a learning-based approach for the modelling of complex movement sequences. Based on the method of Spatio-Temporal Morphable Models (STMMS. We derive a hierarchical algorithm that, in a first step, identifies automatically movement elements in movement sequences based on a coarse spatio-temporal description, and in a second step models these movement primitives by approximation through linear combinations of learned example movement trajectories. We describe the different steps of the algorithm and show how it can be applied for modelling and synthesis of complex sequences of human movements that contain movement elements with variable style. The proposed method is demonstrated on different applications of movement representation relevant for imitation learning of movement styles in humanoid robotics.

ei

PDF [BibTex]

PDF [BibTex]


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Statistical Learning Theory, Capacity and Complexity

Schölkopf, B.

Complexity, 8(4):87-94, July 2003 (article)

Abstract
We give an exposition of the ideas of statistical learning theory, followed by a discussion of how a reinterpretation of the insights of learning theory could potentially also benefit our understanding of a certain notion of complexity.

ei

Web DOI [BibTex]


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Dealing with large Diagonals in Kernel Matrices

Weston, J., Schölkopf, B., Eskin, E., Leslie, C., Noble, W.

Annals of the Institute of Statistical Mathematics, 55(2):391-408, June 2003 (article)

Abstract
In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well: We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine, which is a common kernel approach for pattern recognition.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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The em Algorithm for Kernel Matrix Completion with Auxiliary Data

Tsuda, K., Akaho, S., Asai, K.

Journal of Machine Learning Research, 4, pages: 67-81, May 2003 (article)

ei

PDF [BibTex]

PDF [BibTex]


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Constructing Descriptive and Discriminative Non-linear Features: Rayleigh Coefficients in Kernel Feature Spaces

Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5):623-628, May 2003 (article)

Abstract
We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh coefficient, we propose nonlinear generalizations of Fisher‘s discriminant and oriented PCA using support vector kernel functions. Extensive simulations show the utility of our approach.

ei

DOI [BibTex]

DOI [BibTex]


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A case based comparison of identification with neural network and Gaussian process models.

Kocijan, J., Banko, B., Likar, B., Girard, A., Murray-Smith, R., Rasmussen, CE.

In Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS 2003, 1, pages: 137-142, (Editors: Ruano, E.A.), Proceedings of the International Conference on Intelligent Control Systems and Signal Processing ICONS, April 2003 (inproceedings)

Abstract
In this paper an alternative approach to black-box identification of non-linear dynamic systems is compared with the more established approach of using artificial neural networks. The Gaussian process prior approach is a representative of non-parametric modelling approaches. It was compared on a pH process modelling case study. The purpose of modelling was to use the model for control design. The comparison revealed that even though Gaussian process models can be effectively used for modelling dynamic systems caution has to be axercised when signals are selected.

ei

PDF [BibTex]

PDF [BibTex]


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On-Line One-Class Support Vector Machines. An Application to Signal Segmentation

Gretton, A., Desobry, ..

In IEEE ICASSP Vol. 2, pages: 709-712, IEEE ICASSP, April 2003 (inproceedings)

Abstract
In this paper, we describe an efficient algorithm to sequentially update a density support estimate obtained using one-class support vector machines. The solution provided is an exact solution, which proves to be far more computationally attractive than a batch approach. This deterministic technique is applied to the problem of audio signal segmentation, with simulations demonstrating the computational performance gain on toy data sets, and the accuracy of the segmentation on audio signals.

ei

PostScript [BibTex]

PostScript [BibTex]


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The Kernel Mutual Information

Gretton, A., Herbrich, R., Smola, A.

In IEEE ICASSP Vol. 4, pages: 880-883, IEEE ICASSP, April 2003 (inproceedings)

Abstract
We introduce a new contrast function, the kernel mutual information (KMI), to measure the degree of independence of continuous random variables. This contrast function provides an approximate upper bound on the mutual information, as measured near independence, and is based on a kernel density estimate of the mutual information between a discretised approximation of the continuous random variables. We show that Bach and Jordan‘s kernel generalised variance (KGV) is also an upper bound on the same kernel density estimate, but is looser. Finally, we suggest that the addition of a regularising term in the KGV causes it to approach the KMI, which motivates the introduction of this regularisation.

ei

PostScript [BibTex]

PostScript [BibTex]


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Tractable Inference for Probabilistic Data Models

Csato, L., Opper, M., Winther, O.

Complexity, 8(4):64-68, April 2003 (article)

Abstract
We present an approximation technique for probabilistic data models with a large number of hidden variables, based on ideas from statistical physics. We give examples for two nontrivial applications. © 2003 Wiley Periodicals, Inc.

ei

PDF GZIP Web [BibTex]

PDF GZIP Web [BibTex]