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2015


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Unique high-temperature performance of highly consensed MnBi permanent magnets

Chen, Y., Gregori, G., Leineweber, A., Qu, F., Chen, C., Tietze, T., Kronmüller, H., Schütz, G., Goering, E.

{Scripta Materialia}, 107, pages: 131-135, Pergamon, Tarrytown, NY, 2015 (article)

mms

DOI [BibTex]

2015


DOI [BibTex]


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Quantifying Emergent Behavior of Autonomous Robots

Martius, G., Olbrich, E.

Entropy, 17(10):7266, 2015 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Super-Resolution Keyframe Fusion for 3D Modeling with High-Quality Textures

Maier, R., Stueckler, J., Cremers, D.

In International Conference on 3D Vision (3DV), October 2015, {[slides] [poster]} (inproceedings)

ev

[BibTex]

[BibTex]


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Utility-weighted sampling in decisions from experience

Lieder, F., Griffiths, T. L., Hsu, M.

In The 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2015 (inproceedings)

re

[BibTex]

[BibTex]


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Nachhaltige Effekte simulatorbasierten Trainings auf eine ökologische Fahrweise [Sustainable effects of simulator-based training on ecological driving]

Lüderitz, C., Wirzberger, M., Karrer-Gauß, K.

In VerANTWORTung für die Arbeit der Zukunft, 61st Conference of the Society for Ergonomics and Work Science, GfA Press, Dortmund, 2015 (inproceedings)

re

[BibTex]

[BibTex]


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Electrical determination of vortex state in submicron magnetic elements

Gangwar, A., Bauer, H. G., Chauleau, J., Noske, M., Weigand, M., Stoll, H., Schütz, G., Back, C. H.

{Physical Review B}, 91(9), American Physical Society, Woodbury, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Mechanisms for the symmetric and antisymmetric switching of a magnetic vortex core: Differences and common aspects

Noske, M., Stoll, H., Fähnle, M., Hertel, R., Schütz, G.

{Physical Review B}, 91(1), American Physical Society, Woodbury, NY, 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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Automotive domain wall propagation in ferromagnetic rings

Richter, K., Mawass, M., Krone, A., Krüger, B., Weigand, M., Schütz, G., Stoll, H., Kläui, M.

In IEEE International Magnetics Conference (INTERMAG 2015), IEEE, Beijing, China, 2015 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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High resolution, high efficiency mulitlayer Fresnel zone plates for soft and hard X-rays

Sanli, U., Keskinbora, K., Gregorczyk, K., Leister, J., Teeny, N., Grévent, C., Knez, M., Schütz, G.

{Proceedings of SPIE}, 9592, SPIE, Bellingham, Washington, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Macroscopic drift current in the inverse Faraday effect

Hertel, R., Fähnle, M.

{Physical Review B}, 91(2), American Physical Society, Woodbury, NY, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Single-step 3D nanofabrication of kinoform optics via gray-scale focused ion beam lithography for efficient X-ray focusing

Keskinbora, K., Grévent, C., Hirscher, M., Weigand, M., Schütz, G.

{Advanced Optical Materials}, 3, pages: 792-800, WILEY-VCH Verlag GmbH Co. KGaA, Weinheim, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Band structure engineering of two-dimensional magnonic vortex crystals

Behncke, C., Hänze, M., Adolff, C. F., Weigand, M., Meier, G.

{Physical Review B}, 91(22), American Physical Society, Woodbury, NY, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Towards denoising XMCD movies of fast magnetization dynamics using extended Kalman filter

Kopp, M., Harmeling, S., Schütz, G., Schölkopf, B., Fähnle, M.

{Ultramicroscopy}, 148, pages: 115-122, North-Holland, Amsterdam, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Humanoid Momentum Estimation Using Sensed Contact Wrenches

Rotella, N., Herzog, A., Schaal, S., Righetti, L.

In 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pages: 556-563, IEEE, Seoul, South Korea, 2015 (inproceedings)

Abstract
This work presents approaches for the estimation of quantities important for the control of the momentum of a humanoid robot. In contrast to previous approaches which use simplified models such as the Linear Inverted Pendulum Model, we present estimators based on the momentum dynamics of the robot. By using this simple yet dynamically-consistent model, we avoid the issues of using simplified models for estimation. We develop an estimator for the center of mass and full momentum which can be reformulated to estimate center of mass offsets as well as external wrenches applied to the robot. The observability of these estimators is investigated and their performance is evaluated in comparison to previous approaches.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker

Leibfried, F, Braun, DA

Neural Computation, 27(8):1686-1720, July 2015 (article)

Abstract
Rate distortion theory describes how to communicate relevant information most efficiently over a channel with limited capacity. One of the many applications of rate distortion theory is bounded rational decision making, where decision makers are modeled as information channels that transform sensory input into motor output under the constraint that their channel capacity is limited. Such a bounded rational decision maker can be thought to optimize an objective function that trades off the decision maker's utility or cumulative reward against the information processing cost measured by the mutual information between sensory input and motor output. In this study, we interpret a spiking neuron as a bounded rational decision maker that aims to maximize its expected reward under the computational constraint that the mutual information between the neuron's input and output is upper bounded. This abstract computational constraint translates into a penalization of the deviation between the neuron's instantaneous and average firing behavior. We derive a synaptic weight update rule for such a rate distortion optimizing neuron and show in simulations that the neuron efficiently extracts reward-relevant information from the input by trading off its synaptic strengths against the collected reward.

ei

DOI [BibTex]

DOI [BibTex]


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Magnetic moments induce strong phonon renormalization in FeSi

Krannich, S., Sidis, Y., Lamago, D., Heid, R., Mignot, J., von Löhneysen, H., Ivanov, A., Steffens, P., Keller, T., Wang, L., Goering, E., Weber, F.

{Nature Communications}, 6, Nature Publishing Group, London, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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

Tsatsoulis, T.

Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

mms

[BibTex]

[BibTex]


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Robot Learning

Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J. A., Schaal, S.

In Springer Handbook of Robotics 2nd Edition, pages: 1371-1394, Springer Berlin Heidelberg, Berlin, Heidelberg, 2015 (incollection)

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[BibTex]

[BibTex]


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Reconstructing Street-Scenes in Real-Time From a Driving Car

Usenko, V., Engel, J., Stueckler, J., Cremers, D.

In Proc. of the Int. Conference on 3D Vision (3DV), October 2015 (inproceedings)

ev

[BibTex]

[BibTex]


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Model-based strategy selection learning

Lieder, F., Griffiths, T. L.

The 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2015 (article)

re

Project Page [BibTex]

Project Page [BibTex]


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Cognitive modeling meets instructional design: Exploring Cognitive Load Theory with ACT-R

Wirzberger, M., Rey, G. D.

In Trends in Neuroergonomics. Proceedings of the 11th Berlin Workshop Human-Machine Systems, pages: 190-193, Universitätsverlag der TU Berlin, Berlin, 2015 (inproceedings)

re

DOI [BibTex]

DOI [BibTex]


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What is epistemic value in free energy models of learning and acting? A bounded rationality perspective

Ortega, PA, Braun, DA

Cognitive Neuroscience, 6(4):215-216, December 2015 (article)

Abstract
Free energy models of learning and acting do not only care about utility or extrinsic value, but also about intrinsic value, that is, the information value stemming from probability distributions that represent beliefs or strategies. While these intrinsic values can be interpreted as epistemic values or exploration bonuses under certain conditions, the framework of bounded rationality offers a complementary interpretation in terms of information-processing costs that we discuss here.

ei

DOI [BibTex]

DOI [BibTex]


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

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

2015 (misc)

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link (url) [BibTex]


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Quantum kinetic theory of ultrafast demagnetization by electron-phonon scattering

Briones Paz, J. Z.

Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

mms

[BibTex]

[BibTex]


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Perpendicular magnetisation from in-plane fields in nano-scaled antidot lattices

Gräfe, J., Haering, F., Tietze, T., Audehm, P., Weigand, M., Wiedwald, U., Ziemann, P., Gawronski, P., Schütz, G., Goering, E. J.

{Nanotechnology}, 26(22), IOP Pub., Bristol, UK, 2015 (article)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Theory of ultrafast demagnetization after femtosecond laser pulses

Fähnle, M., Illg, C., Haag, M., Teeny, N.

{Acta Physica Polonica A}, 127(2):170-175, Państwowe Wydawnictwo Naukowe, Warszawa, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Non-linear radial spinwave modes in thin magnetic disks

Helsen, M., Gangwar, Ajay, De Clercq, J., Vansteenkiste, A., Weigand, M., Back, C. H., Van Waeyenberge, B.

{Applied Physics Letters}, 106(3), American Institute of Physics, Melville, NY, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Hydrogen isotope separation in metal-organic frameworks: Kinetic or chemical affinity quantum-sieving?

Savchenko, I., Mavrandonakis, A., Heine, T., Oh, H., Teufel, J., Hirscher, M.

{Microporous and Mesoporous Materials}, 216, pages: 133-137, Elsevier, Amsterdam, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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High-resolution dichroic imaging of magnetic flux distributions in superconductors with scanning x-ray microscopy

Ruoß, S., Stahl, C., Weigand, M., Schütz, G., Albrecht, J.

{Applied Physics Letters}, 106, American Institute of Physics, Melville, NY, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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The third dimension: Vortex core reversal by interaction with \textquotesingleflexure modes’

Noske, M., Stoll, H., Fähnle, M., Weigand, M., Dieterle, G., Förster, J., Gangwar, A., Slavin, A., Back, C. H., Schütz, G.

In IEEE International Magnetics Conference (INTERMAG 2015), IEEE, Beijing, China, 2015 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]


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Preparation and characterisation of epitaxial Pt/Cu/FeMn/Co thin films on (100)-oriented MgO single crystals

Schmidt, M., Gräfe, J., Audehm, P., Phillipp, F., Schütz, G., Goering, E.

{Physica Status Solidi A}, 212(10):2114-2123, Wiley-VCH, Weinheim, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Probing the magnetic moments of [MnIII6CrIII]3+ single-molecule magnets - A cross comparison of XMCD and spin-resolved electron spectroscopy

Helmstedt, A., Dohmeier, N., Müller, N., Gryzia, A., Brechling, A., Heinzmann, U., Hoeke, V., Krickemeyer, E., Glaser, T., Leicht, P., Fonin, M., Tietze, T., Joly, L., Kuepper, K.

{Journal of Electron Spectroscopy and Related Phenomena}, 198, pages: 12-19, Elsevier B.V., Amsterdam, 2015 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Skyrmions at room temperature in magnetic multilayers

Moreau-Luchaire, C., Reyren, N., Moutafis, C., Sampaio, J., Van Horne, N., Vaz, C. A., Warnicke, P., Garcia, K., Weigand, M., Bouzehouane, K., Deranlot, C., George, J., Raabe, J., Cros, V., Fert, A.

In IEEE International Magnetics Conference (INTERMAG 2015), IEEE, Beijing, China, 2015 (inproceedings)

mms

DOI [BibTex]

DOI [BibTex]

2009


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Learning an Interactive Segmentation System

Nickisch, H., Kohli, P., Rother, C.

Max Planck Institute for Biological Cybernetics, December 2009 (techreport)

Abstract
Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user - a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.

ei

Web [BibTex]

2009


Web [BibTex]


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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.

ei

PDF Web Web [BibTex]

PDF Web Web [BibTex]


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Learning Probabilistic Models via Bayesian Inverse Planning

Boularias, A., Chaib-Draa, B.

NIPS Workshop on Probabilistic Approaches for Robotics and Control, December 2009 (poster)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Efficient Subwindow Search: A Branch and Bound Framework for Object Localization

Lampert, C., Blaschko, M., Hofmann, T.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12):2129-2142, December 2009 (article)

Abstract
Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object‘s location, one can take a sliding window approach, but this strongly increases the computational cost because the classifier or similarity function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages. It converges to a globally optimal solution typically in linear or even sublinear time, in contrast to the quadratic scaling of exhaustive or sliding window search. We show how our method is applicable to different object detection and image retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest-neighbor classifiers based on the chi^2 distance. We demonstrate state-of-the-art localization performance of the resulting systems on the UIUC Cars data set, the PASCAL VOC 2006 data set, and in the PASCAL VOC 2007 competition.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Bayesian Quadratic Reinforcement Learning

Hennig, P., Stern, D., Graepel, T.

NIPS Workshop on Probabilistic Approaches for Robotics and Control, December 2009 (poster)

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PDF Web [BibTex]

PDF Web [BibTex]


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A computational model of human table tennis for robot application

Mülling, K., Peters, J.

In AMS 2009, pages: 57-64, (Editors: Dillmann, R. , J. Beyerer, C. Stiller, M. Zöllner, T. Gindele), Springer, Berlin, Germany, Autonome Mobile Systeme, December 2009 (inproceedings)

Abstract
Table tennis is a difficult motor skill which requires all basic components of a general motor skill learning system. In order to get a step closer to such a generic approach to the automatic acquisition and refinement of table tennis, we study table tennis from a human motor control point of view. We make use of the basic models of discrete human movement phases, virtual hitting points, and the operational timing hypothesis. Using these components, we create a computational model which is aimed at reproducing human-like behavior. We verify the functionality of this model in a physically realistic simulation of a BarrettWAM.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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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.

ei

PDF Web Web [BibTex]

PDF Web Web [BibTex]


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A second order sliding mode controller with polygonal constraints

Dinuzzo, F.

In pages: 6715-6719, IEEE, Piscataway, NJ, USA, 48th IEEE Conference on Decision and Control (CDC), December 2009 (inproceedings)

Abstract
It is presented a discontinuous controller that ensure uniform finite-time zero stabilization of the output for uncertain SISO systems of relative degree two, while keeping the auxiliary system state within a prescribed convex polygon. The proposed method extends applicability of second order sliding modes controllers to the case of uncertain dynamical systems with constraints.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Generation of three-dimensional random rotations in fitting and matching problems

Habeck, M.

Computational Statistics, 24(4):719-731, December 2009 (article)

Abstract
An algorithm is developed to generate random rotations in three-dimensional space that follow a probability distribution arising in fitting and matching problems. The rotation matrices are orthogonally transformed into an optimal basis and then parameterized using Euler angles. The conditional distributions of the three Euler angles have a very simple form: the two azimuthal angles can be decoupled by sampling their sum and difference from a von Mises distribution; the cosine of the polar angle is exponentially distributed and thus straighforward to generate. Simulation results are shown and demonstrate the effectiveness of the method. The algorithm is compared to other methods for generating random rotations such as a random walk Metropolis scheme and a Gibbs sampling algorithm recently introduced by Green and Mardia. Finally, the algorithm is applied to a probabilistic version of the Procrustes problem of fitting two point sets and applied in the context of protein structure superposition.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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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.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Adaptive Importance Sampling for Value Function Approximation in Off-policy Reinforcement Learning

Hachiya, H., Akiyama, T., Sugiyama, M., Peters, J.

Neural Networks, 22(10):1399-1410, December 2009 (article)

Abstract
Off-policy reinforcement learning is aimed at efficiently using data samples gathered from a policy that is different from the currently optimized policy. A common approach is to use importance sampling techniques for compensating for the bias of value function estimators caused by the difference between the data-sampling policy and the target policy. However, existing off-policy methods often do not take the variance of the value function estimators explicitly into account and therefore their performance tends to be unstable. To cope with this problem, we propose using an adaptive importance sampling technique which allows us to actively control the trade-off between bias and variance. We further provide a method for optimally determining the trade-off parameter based on a variant of cross-validation. We demonstrate the usefulness of the proposed approach through simulations.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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A PAC-Bayesian Approach to Formulation of Clustering Objectives

Seldin, Y., Tishby, N.

In Proceedings of the NIPS 2009 Workshop "Clustering: Science or Art? Towards Principled Approaches", pages: 1-4, NIPS Workshop "Clustering: Science or Art? Towards Principled Approaches", December 2009 (inproceedings)

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.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Policy Transfer in Apprenticeship Learning

Boularias, A., Chaib-Draa, B.

NIPS Workshop on Transfer Learning for Structured Data (TLSD-09), December 2009 (poster)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Notes on Graph Cuts with Submodular Edge Weights

Jegelka, S., Bilmes, J.

In pages: 1-6, NIPS Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra (DISCML), December 2009 (inproceedings)

Abstract
Generalizing the cost in the standard min-cut problem to a submodular cost function immediately makes the problem harder. Not only do we prove NP hardness even for nonnegative submodular costs, but also show a lower bound of (|V |1/3) on the approximation factor for the (s, t) cut version of the problem. On the positive side, we propose and compare three approximation algorithms with an overall approximation factor of O(min{|V |,p|E| log |V |}) that appear to do well in practice.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Guest editorial: special issue on structured prediction

Parker, C., Altun, Y., Tadepalli, P.

Machine Learning, 77(2-3):161-164, December 2009 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Structured prediction by joint kernel support estimation

Lampert, CH., Blaschko, MB.

Machine Learning, 77(2-3):249-269, December 2009 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]