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2013


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Puppet Flow

Zuffi, S., Black, M. J.

(7), Max Planck Institute for Intelligent Systems, October 2013 (techreport)

Abstract
We introduce Puppet Flow (PF), a layered model describing the optical flow of a person in a video sequence. We consider video frames composed by two layers: a foreground layer corresponding to a person, and background. We model the background as an affine flow field. The foreground layer, being a moving person, requires reasoning about the articulated nature of the human body. We thus represent the foreground layer with the Deformable Structures model (DS), a parametrized 2D part-based human body representation. We call the motion field defined through articulated motion and deformation of the DS model, a Puppet Flow. By exploiting the DS representation, Puppet Flow is a parametrized optical flow field, where parameters are the person's pose, gender and body shape.

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pdf Project Page Project Page [BibTex]

2013


pdf Project Page Project Page [BibTex]


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Learning and Optimization with Submodular Functions

Sankaran, B., Ghazvininejad, M., He, X., Kale, D., Cohen, L.

ArXiv, May 2013 (techreport)

Abstract
In many naturally occurring optimization problems one needs to ensure that the definition of the optimization problem lends itself to solutions that are tractable to compute. In cases where exact solutions cannot be computed tractably, it is beneficial to have strong guarantees on the tractable approximate solutions. In order operate under these criterion most optimization problems are cast under the umbrella of convexity or submodularity. In this report we will study design and optimization over a common class of functions called submodular functions. Set functions, and specifically submodular set functions, characterize a wide variety of naturally occurring optimization problems, and the property of submodularity of set functions has deep theoretical consequences with wide ranging applications. Informally, the property of submodularity of set functions concerns the intuitive principle of diminishing returns. This property states that adding an element to a smaller set has more value than adding it to a larger set. Common examples of submodular monotone functions are entropies, concave functions of cardinality, and matroid rank functions; non-monotone examples include graph cuts, network flows, and mutual information. In this paper we will review the formal definition of submodularity; the optimization of submodular functions, both maximization and minimization; and finally discuss some applications in relation to learning and reasoning using submodular functions.

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arxiv link (url) [BibTex]

arxiv link (url) [BibTex]


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A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them

Sun, D., Roth, S., Black, M. J.

(CS-10-03), Brown University, Department of Computer Science, January 2013 (techreport)

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pdf [BibTex]

pdf [BibTex]


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A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs)

Grosse-Wentrup, M., Schölkopf, B.

In Brain-Computer Interface Research, pages: 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 (inbook)

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PDF DOI [BibTex]

PDF DOI [BibTex]


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Semi-supervised learning in causal and anticausal settings

Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J.

In Empirical Inference, pages: 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

ei

DOI [BibTex]

DOI [BibTex]


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Tractable large-scale optimization in machine learning

Sra, S.

In Tractability: Practical Approaches to Hard Problems, pages: 202-230, 7, (Editors: Bordeaux, L., Hamadi , Y., Kohli, P. and Mateescu, R. ), Cambridge University Press , 2013 (inbook)

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[BibTex]

[BibTex]


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Animating Samples from Gaussian Distributions

Hennig, P.

(8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 (techreport)

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PDF [BibTex]

PDF [BibTex]


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Maximizing Kepler science return per telemetered pixel: Detailed models of the focal plane in the two-wheel era

Hogg, D. W., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Lang, D., Montet, B. T., Schiminovich, D., Schölkopf, B.

arXiv:1309.0653, 2013 (techreport)

ei

link (url) [BibTex]

link (url) [BibTex]


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Maximizing Kepler science return per telemetered pixel: Searching the habitable zones of the brightest stars

Montet, B. T., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Hogg, D. W., Lang, D., Schiminovich, D., Schölkopf, B.

arXiv:1309.0654, 2013 (techreport)

ei

link (url) [BibTex]

link (url) [BibTex]


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On the Relations and Differences between Popper Dimension, Exclusion Dimension and VC-Dimension

Seldin, Y., Schölkopf, B.

In Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, pages: 53-57, 6, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

ei

[BibTex]

[BibTex]


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Behavior as broken symmetry in embodied self-organizing robots

Der, R., Martius, G.

In Advances in Artificial Life, ECAL 2013, pages: 601-608, MIT Press, 2013 (incollection)

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[BibTex]

[BibTex]


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Using Torque Redundancy to Optimize Contact Forces in Legged Robots

Righetti, L., Buchli, J., Mistry, M., Kalakrishnan, M., Schaal, S.

In Redundancy in Robot Manipulators and Multi-Robot Systems, 57, pages: 35-51, Lecture Notes in Electrical Engineering, Springer Berlin Heidelberg, 2013 (incollection)

Abstract
The development of legged robots for complex environments requires controllers that guarantee both high tracking performance and compliance with the environment. More specifically the control of contact interaction with the environment is of crucial importance to ensure stable, robust and safe motions. In the following, we present an inverse dynamics controller that exploits torque redundancy to directly and explicitly minimize any combination of linear and quadratic costs in the contact constraints and in the commands. Such a result is particularly relevant for legged robots as it allows to use torque redundancy to directly optimize contact interactions. For example, given a desired locomotion behavior, it can guarantee the minimization of contact forces to reduce slipping on difficult terrains while ensuring high tracking performance of the desired motion. The proposed controller is very simple and computationally efficient, and most importantly it can greatly improve the performance of legged locomotion on difficult terrains as can be seen in the experimental results.

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link (url) [BibTex]

link (url) [BibTex]


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Class-Specific Hough Forests for Object Detection

Gall, J., Lempitsky, V.

In Decision Forests for Computer Vision and Medical Image Analysis, pages: 143-157, 11, (Editors: Criminisi, A. and Shotton, J.), Springer, 2013 (incollection)

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code Project Page [BibTex]

code Project Page [BibTex]

2010


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Computationally efficient algorithms for statistical image processing: Implementation in R

Langovoy, M., Wittich, O.

(2010-053), EURANDOM, Technische Universiteit Eindhoven, December 2010 (techreport)

Abstract
In the series of our earlier papers on the subject, we proposed a novel statistical hy- pothesis testing method for detection of objects in noisy images. The method uses results from percolation theory and random graph theory. We developed algorithms that allowed to detect objects of unknown shapes in the presence of nonparametric noise of unknown level and of un- known distribution. No boundary shape constraints were imposed on the objects, only a weak bulk condition for the object's interior was required. Our algorithms have linear complexity and exponential accuracy. In the present paper, we describe an implementation of our nonparametric hypothesis testing method. We provide a program that can be used for statistical experiments in image processing. This program is written in the statistical programming language R.

ei

PDF [BibTex]

2010


PDF [BibTex]


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Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

Seeger, M., Nickisch, H.

Max Planck Institute for Biological Cybernetics, December 2010 (techreport)

Abstract
We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method is provably convergent. By marrying convergent EP ideas from (Opper&Winther 05) with covariance decoupling techniques (Wipf&Nagarajan 08, Nickisch&Seeger 09), it runs at least an order of magnitude faster than the most commonly used EP solver.

ei

Web [BibTex]

Web [BibTex]


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Markerless tracking of Dynamic 3D Scans of Faces

Walder, C., Breidt, M., Bülthoff, H., Schölkopf, B., Curio, C.

In Dynamic Faces: Insights from Experiments and Computation, pages: 255-276, (Editors: Curio, C., Bülthoff, H. H. and Giese, M. A.), MIT Press, Cambridge, MA, USA, December 2010 (inbook)

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Web [BibTex]

Web [BibTex]


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Policy Gradient Methods

Peters, J., Bagnell, J.

In Encyclopedia of Machine Learning, pages: 774-776, (Editors: Sammut, C. and Webb, G. I.), Springer, Berlin, Germany, December 2010 (inbook)

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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A PAC-Bayesian Analysis of Graph Clustering and Pairwise Clustering

Seldin, Y.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2010 (techreport)

Abstract
We formulate weighted graph clustering as a prediction problem: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. This formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph clustering with other possible ways to model the graph. We adapt the PAC-Bayesian analysis of co-clustering (Seldin and Tishby, 2008; Seldin, 2009) to derive a PAC-Bayesian generalization bound for graph clustering. The bound shows that graph clustering should optimize a trade-off between empirical data fit and the mutual information that clusters preserve on the graph nodes. A similar trade-off derived from information-theoretic considerations was already shown to produce state-of-the-art results in practice (Slonim et al., 2005; Yom-Tov and Slonim, 2009). This paper supports the empirical evidence by providing a better theoretical foundation, suggesting formal generalization guarantees, and offering a more accurate way to deal with finite sample issues. We derive a bound minimization algorithm and show that it provides good results in real-life problems and that the derived PAC-Bayesian bound is reasonably tight.

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PDF Web [BibTex]

PDF Web [BibTex]


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Sparse nonnegative matrix approximation: new formulations and algorithms

Tandon, R., Sra, S.

(193), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2010 (techreport)

Abstract
We introduce several new formulations for sparse nonnegative matrix approximation. Subsequently, we solve these formulations by developing generic algorithms. Further, to help selecting a particular sparse formulation, we briefly discuss the interpretation of each formulation. Finally, preliminary experiments are presented to illustrate the behavior of our formulations and algorithms.

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PDF [BibTex]

PDF [BibTex]


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Robust nonparametric detection of objects in noisy images

Langovoy, M., Wittich, O.

(2010-049), EURANDOM, Technische Universiteit Eindhoven, September 2010 (techreport)

Abstract
We propose a novel statistical hypothesis testing method for detection of objects in noisy images. The method uses results from percolation theory and random graph theory. We present an algorithm that allows to detect objects of unknown shapes in the presence of nonparametric noise of unknown level and of unknown distribution. No boundary shape constraints are imposed on the object, only a weak bulk condition for the object's interior is required. The algorithm has linear complexity and exponential accuracy and is appropriate for real-time systems. In this paper, we develop further the mathematical formalism of our method and explore im- portant connections to the mathematical theory of percolation and statistical physics. We prove results on consistency and algorithmic complexity of our testing procedure. In addition, we address not only an asymptotic behavior of the method, but also a nite sample performance of our test.

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PDF [BibTex]

PDF [BibTex]


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Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models

Seeger, M., Nickisch, H.

Max Planck Institute for Biological Cybernetics, August 2010 (techreport)

Abstract
Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher-order Bayesian decision-making problems, such as optimizing image acquisition in magnetic resonance scanners, can be addressed by querying the SLM posterior covariance, unrelated to the density's mode. We propose a scalable algorithmic framework, with which SLM posteriors over full, high-resolution images can be approximated for the first time, solving a variational optimization problem which is convex iff posterior mode finding is convex. These methods successfully drive the optimization of sampling trajectories for real-world magnetic resonance imaging through Bayesian experimental design, which has not been attempted before. Our methodology provides new insight into similarities and differences between sparse reconstruction and approximate Bayesian inference, and has important implications for compressive sensing of real-world images.

ei

Web [BibTex]


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Cooperative Cuts for Image Segmentation

Jegelka, S., Bilmes, J.

(UWEETR-1020-0003), University of Washington, Washington DC, USA, August 2010 (techreport)

Abstract
We propose a novel framework for graph-based cooperative regularization that uses submodular costs on graph edges. We introduce an efficient iterative algorithm to solve the resulting hard discrete optimization problem, and show that it has a guaranteed approximation factor. The edge-submodular formulation is amenable to the same extensions as standard graph cut approaches, and applicable to a range of problems. We apply this method to the image segmentation problem. Specifically, Here, we apply it to introduce a discount for homogeneous boundaries in binary image segmentation on very difficult images, precisely, long thin objects and color and grayscale images with a shading gradient. The experiments show that significant portions of previously truncated objects are now preserved.

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Web [BibTex]

Web [BibTex]


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Fast algorithms for total-variationbased optimization

Barbero, A., Sra, S.

(194), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2010 (techreport)

Abstract
We derive a number of methods to solve efficiently simple optimization problems subject to a totalvariation (TV) regularization, under different norms of the TV operator and both for the case of 1-dimensional and 2-dimensional data. In spite of the non-smooth, non-separable nature of the TV terms considered, we show that a dual formulation with strong structure can be derived. Taking advantage of this structure we develop adaptions of existing algorithms from the optimization literature, resulting in efficient methods for the problem at hand. Experimental results show that for 1-dimensional data the proposed methods achieve convergence within good accuracy levels in practically linear time, both for L1 and L2 norms. For the more challenging 2-dimensional case a performance of order O(N2 log2 N) for N x N inputs is achieved when using the L2 norm. A final section suggests possible extensions and lines of further work.

ei

PDF [BibTex]

PDF [BibTex]


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Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

Nickisch, H., Rasmussen, C.

Max Planck Institute for Biological Cybernetics, June 2010 (techreport)

Abstract
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets.

ei

Web [BibTex]

Web [BibTex]


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Generalized Proximity and Projection with Norms and Mixed-norms

Sra, S.

(192), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, May 2010 (techreport)

Abstract
We discuss generalized proximity operators (GPO) and their associated generalized projection problems. On inputs of size n, we show how to efficiently apply GPOs and generalized projections for separable norms and distance-like functions to accuracy e in O(n log(1/e)) time. We also derive projection algorithms that run theoretically in O(n log n log(1/e)) time but can for suitable parameter ranges empirically outperform the O(n log(1/e)) projection method. The proximity and projection tasks are either separable, and solved directly, or are reduced to a single root-finding step. We highlight that as a byproduct, our analysis also yields an O(n log(1/e)) (weakly linear-time) procedure for Euclidean projections onto the l1;1-norm ball; previously only an O(n log n) method was known. We provide empirical evaluation to illustrate the performance of our methods, noting that for the l1;1-norm projection, our implementation is more than two orders of magnitude faster than the previously known method.

ei

PDF [BibTex]

PDF [BibTex]


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Cooperative Cuts: Graph Cuts with Submodular Edge Weights

Jegelka, S., Bilmes, J.

(189), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, March 2010 (techreport)

Abstract
We introduce a problem we call Cooperative cut, where the goal is to find a minimum-cost graph cut but where a submodular function is used to define the cost of a subsets of edges. That means, the cost of an edge that is added to the current cut set C depends on the edges in C. This generalization of 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 Omega(|V|^(1/3)) on the approximation factor for the problem. On the positive side, we propose and compare four approximation algorithms with an overall approximation factor of min { |V|/2, |C*|, O( sqrt(|E|) log |V|), |P_max|}, where C* is the optimal solution, and P_max is the longest s, t path across the cut between given s, t. We also introduce additional heuristics for the problem which have attractive properties from the perspective of practical applications and implementations in that existing fast min-cut libraries may be used as subroutines. Both our approximation algorithms, and our heuristics, appear to do well in practice.

ei

PDF [BibTex]

PDF [BibTex]


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Learning Continuous Grasp Affordances by Sensorimotor Exploration

Detry, R., Baseski, E., Popovic, M., Touati, Y., Krüger, N., Kroemer, O., Peters, J., Piater, J.

In From Motor Learning to Interaction Learning in Robots, pages: 451-465, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010 (inbook)

Abstract
We develop means of learning and representing object grasp affordances probabilistically. By grasp affordance, we refer to an entity that is able to assess whether a given relative object-gripper configuration will yield a stable grasp. These affordances are represented with grasp densities, continuous probability density functions defined on the space of 3D positions and orientations. Grasp densities are registered with a visual model of the object they characterize. They are exploited by aligning them to a target object using visual pose estimation. Grasp densities are refined through experience: A robot “plays” with an object by executing grasps drawn randomly for the object’s grasp density. The robot then uses the outcomes of these grasps to build a richer density through an importance sampling mechanism. Initial grasp densities, called hypothesis densities, are bootstrapped from grasps collected using a motion capture system, or from grasps generated from the visual model of the object. Refined densities, called empirical densities, represent affordances that have been confirmed through physical experience. The applicability of our method is demonstrated by producing empirical densities for two object with a real robot and its 3-finger hand. Hypothesis densities are created from visual cues and human demonstration.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling

Kober, J., Mohler, B., Peters, J.

In From Motor Learning to Interaction Learning in Robots, pages: 209-225, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010 (inbook)

Abstract
Traditional motor primitive approaches deal largely with open-loop policies which can only deal with small perturbations. In this paper, we present a new type of motor primitive policies which serve as closed-loop policies together with an appropriate learning algorithm. Our new motor primitives are an augmented version version of the dynamical system-based motor primitives [Ijspeert et al(2002)Ijspeert, Nakanishi, and Schaal] that incorporates perceptual coupling to external variables. We show that these motor primitives can perform complex tasks such as Ball-in-a-Cup or Kendama task even with large variances in the initial conditions where a skilled human player would be challenged. We initialize the open-loop policies by imitation learning and the perceptual coupling with a handcrafted solution. We first improve the open-loop policies and subsequently the perceptual coupling using a novel reinforcement learning method which is particularly well-suited for dynamical system-based motor primitives.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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From Motor Learning to Interaction Learning in Robots

Sigaud, O., Peters, J.

In From Motor Learning to Interaction Learning in Robots, pages: 1-12, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010 (inbook)

Abstract
The number of advanced robot systems has been increasing in recent years yielding a large variety of versatile designs with many degrees of freedom. These robots have the potential of being applicable in uncertain tasks outside wellstructured industrial settings. However, the complexity of both systems and tasks is often beyond the reach of classical robot programming methods. As a result, a more autonomous solution for robot task acquisition is needed where robots adaptively adjust their behaviour to the encountered situations and required tasks. Learning approaches pose one of the most appealing ways to achieve this goal. However, while learning approaches are of high importance for robotics, we cannot simply use off-the-shelf methods from the machine learning community as these usually do not scale into the domains of robotics due to excessive computational cost as well as a lack of scalability. Instead, domain appropriate approaches are needed. In this book, we focus on several core domains of robot learning. For accurate task execution, we need motor learning capabilities. For fast learning of the motor tasks, imitation learning offers the most promising approach. Self improvement requires reinforcement learning approaches that scale into the domain of complex robots. Finally, for efficient interaction of humans with robot systems, we will need a form of interaction learning. This chapter provides a general introduction to these issues and briefly presents the contributions of the subsequent chapters to the corresponding research topics.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Real-Time Local GP Model Learning

Nguyen-Tuong, D., Seeger, M., Peters, J.

In From Motor Learning to Interaction Learning in Robots, 264, pages: 193-207, Studies in Computational Intelligence, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010 (inbook)

Abstract
For many applications in robotics, accurate dynamics models are essential. However, in some applications, e.g., in model-based tracking control, precise dynamics models cannot be obtained analytically for sufficiently complex robot systems. In such cases, machine learning offers a promising alternative for approximating the robot dynamics using measured data. However, standard regression methods such as Gaussian process regression (GPR) suffer from high computational complexity which prevents their usage for large numbers of samples or online learning to date. In this paper, we propose an approximation to the standard GPR using local Gaussian processes models inspired by [Vijayakumar et al(2005)Vijayakumar, D’Souza, and Schaal, Snelson and Ghahramani(2007)]. Due to reduced computational cost, local Gaussian processes (LGP) can be applied for larger sample-sizes and online learning. Comparisons with other nonparametric regressions, e.g., standard GPR, support vector regression (SVR) and locally weighted proje ction regression (LWPR), show that LGP has high approximation accuracy while being sufficiently fast for real-time online learning.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Machine Learning Methods for Automatic Image Colorization

Charpiat, G., Bezrukov, I., Hofmann, M., Altun, Y., Schölkopf, B.

In Computational Photography: Methods and Applications, pages: 395-418, Digital Imaging and Computer Vision, (Editors: Lukac, R.), CRC Press, Boca Raton, FL, USA, 2010 (inbook)

Abstract
We aim to color greyscale images automatically, without any manual intervention. The color proposition could then be interactively corrected by user-provided color landmarks if necessary. Automatic colorization is nontrivial since there is usually no one-to-one correspondence between color and local texture. The contribution of our framework is that we deal directly with multimodality and estimate, for each pixel of the image to be colored, the probability distribution of all possible colors, instead of choosing the most probable color at the local level. We also predict the expected variation of color at each pixel, thus defining a non-uniform spatial coherency criterion. We then use graph cuts to maximize the probability of the whole colored image at the global level. We work in the L-a-b color space in order to approximate the human perception of distances between colors, and we use machine learning tools to extract as much information as possible from a dataset of colored examples. The resulting algorithm is fast, designed to be more robust to texture noise, and is above all able to deal with ambiguity, in contrary to previous approaches.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Approaches Based on Support Vector Machine to Classification of Remote Sensing Data

Bruzzone, L., Persello, C.

In Handbook of Pattern Recognition and Computer Vision, pages: 329-352, (Editors: Chen, C.H.), ICP, London, UK, 2010 (inbook)

Abstract
This chapter presents an extensive and critical review on the use of kernel methods and in particular of support vector machines (SVMs) in the classification of remote-sensing (RS) data. The chapter recalls the mathematical formulation and the main theoretical concepts related to SVMs, and discusses the motivations at the basis of the use of SVMs in remote sensing. A review on the main applications of SVMs in classification of remote sensing is given, presenting a literature survey on the use of SVMs for the analysis of different kinds of RS images. In addition, the most recent methodological developments related to SVM-based classification techniques in RS are illustrated by focusing on semisupervised, domain adaptation, and context sensitive approaches. Finally, the most promising research directions on SVM in RS are identified and discussed.

ei

Web [BibTex]

Web [BibTex]


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Information-theoretic inference of common ancestors

Steudel, B., Ay, N.

Computing Research Repository (CoRR), abs/1010.5720, pages: 18, 2010 (techreport)

ei

Web [BibTex]

Web [BibTex]


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Nanohandling robot cells

Fatikow, Sergej, Wich, Thomas, Dahmen, Christian, Jasper, Daniel, Stolle, Christian, Eichhorn, Volkmar, Hagemann, Saskia, Weigel-Jech, Michael

In Handbook of Nanophysics: Nanomedicine and Nanorobotics, pages: 1-31, CRC Press, 2010 (incollection)

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[BibTex]

[BibTex]


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Locally weighted regression for control

Ting, J., Vijayakumar, S., Schaal, S.

In Encyclopedia of Machine Learning, pages: 613-624, (Editors: Sammut, C.;Webb, G. I.), Springer, 2010, clmc (inbook)

Abstract
This is article addresses two topics: learning control and locally weighted regression.

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link (url) [BibTex]

link (url) [BibTex]


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Physisorption in porous materials

Hirscher, M., Panella, B.

In Handbook of Hydrogen Storage, pages: 39-62, WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim, 2010 (incollection)

mms

[BibTex]

[BibTex]


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Adsorption technologies

Schmitz, B., Hirscher, M.

In Hydrogen and Fuel Cells, pages: 431-445, WILEY-VCH, Weinheim, 2010 (incollection)

mms

[BibTex]

[BibTex]


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Atomic-Force-Microscopy-Based Nanomanipulation Systems

Onal, C. D., Ozcan, O., Sitti, M.

In Handbook of Nanophysics: Nanomedicine and Nanorobotics, pages: 1-15, CRC Press, 2010 (incollection)

pi

[BibTex]

[BibTex]


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Taming the Beast: Guided Self-organization of Behavior in Autonomous Robots

Martius, G., Herrmann, J. M.

In From Animals to Animats 11, 6226, pages: 50-61, LNCS, Springer, 2010 (incollection)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]

2008


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Frequent Subgraph Retrieval in Geometric Graph Databases

Nowozin, S., Tsuda, K.

(180), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2008 (techreport)

Abstract
Discovery of knowledge from geometric graph databases is of particular importance in chemistry and biology, because chemical compounds and proteins are represented as graphs with 3D geometric coordinates. In such applications, scientists are not interested in the statistics of the whole database. Instead they need information about a novel drug candidate or protein at hand, represented as a query graph. We propose a polynomial-delay algorithm for geometric frequent subgraph retrieval. It enumerates all subgraphs of a single given query graph which are frequent geometric epsilon-subgraphs under the entire class of rigid geometric transformations in a database. By using geometric epsilon-subgraphs, we achieve tolerance against variations in geometry. We compare the proposed algorithm to gSpan on chemical compound data, and we show that for a given minimum support the total number of frequent patterns is substantially limited by requiring geometric matching. Although the computation time per pattern is larger than for non-geometric graph mining, the total time is within a reasonable level even for small minimum support.

ei

PDF [BibTex]

2008


PDF [BibTex]


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Simultaneous Implicit Surface Reconstruction and Meshing

Giesen, J., Maier, M., Schölkopf, B.

(179), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2008 (techreport)

Abstract
We investigate an implicit method to compute a piecewise linear representation of a surface from a set of sample points. As implicit surface functions we use the weighted sum of piecewise linear kernel functions. For such a function we can partition Rd in such a way that these functions are linear on the subsets of the partition. For each subset in the partition we can then compute the zero level set of the function exactly as the intersection of a hyperplane with the subset.

ei

PDF [BibTex]

PDF [BibTex]


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Taxonomy Inference Using Kernel Dependence Measures

Blaschko, M., Gretton, A.

(181), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, November 2008 (techreport)

Abstract
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms work by maximizing the dependence between the taxonomy and the original data. The resulting taxonomy is a more informative visualization of complex data than simple clustering; in addition, taking into account the relations between different clusters is shown to substantially improve the quality of the clustering, when compared with state-of-the-art algorithms in the literature (both spectral clustering and a previous dependence maximization approach). We demonstrate our algorithm on image and text data.

ei

PDF [BibTex]

PDF [BibTex]


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Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models

Seeger, M., Nickisch, H.

(175), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2008 (techreport)

ei

PDF [BibTex]

PDF [BibTex]


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Block-Iterative Algorithms for Non-Negative Matrix Approximation

Sra, S.

(176), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2008 (techreport)

Abstract
In this report we present new algorithms for non-negative matrix approximation (NMA), commonly known as the NMF problem. Our methods improve upon the well-known methods of Lee & Seung [19] for both the Frobenius norm as well the Kullback-Leibler divergence versions of the problem. For the latter problem, our results are especially interesting because it seems to have witnessed much lesser algorithmic progress as compared to the Frobenius norm NMA problem. Our algorithms are based on a particular block-iterative acceleration technique for EM, which preserves the multiplicative nature of the updates and also ensures monotonicity. Furthermore, our algorithms also naturally apply to the Bregman-divergence NMA algorithms of Dhillon and Sra [8]. Experimentally, we show that our algorithms outperform the traditional Lee/Seung approach most of the time.

ei

PDF [BibTex]

PDF [BibTex]


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Approximation Algorithms for Bregman Clustering Co-clustering and Tensor Clustering

Sra, S., Jegelka, S., Banerjee, A.

(177), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2008 (techreport)

Abstract
The Euclidean K-means problem is fundamental to clustering and over the years it has been intensely investigated. More recently, generalizations such as Bregman k-means [8], co-clustering [10], and tensor (multi-way) clustering [40] have also gained prominence. A well-known computational difficulty encountered by these clustering problems is the NP-Hardness of the associated optimization task, and commonly used methods guarantee at most local optimality. Consequently, approximation algorithms of varying degrees of sophistication have been developed, though largely for the basic Euclidean K-means (or `1-norm K-median) problem. In this paper we present approximation algorithms for several Bregman clustering problems by building upon the recent paper of Arthur and Vassilvitskii [5]. Our algorithms obtain objective values within a factor O(logK) for Bregman k-means, Bregman co-clustering, Bregman tensor clustering, and weighted kernel k-means. To our knowledge, except for some special cases, approximation algorithms have not been considered for these general clustering problems. There are several important implications of our work: (i) under the same assumptions as Ackermann et al. [1] it yields a much faster algorithm (non-exponential in K, unlike [1]) for information-theoretic clustering, (ii) it answers several open problems posed by [4], including generalizations to Bregman co-clustering, and tensor clustering, (iii) it provides practical and easy to implement methods—in contrast to several other common approximation approaches.

ei

PDF [BibTex]

PDF [BibTex]


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Combining Appearance and Motion for Human Action Classification in Videos

Dhillon, P., Nowozin, S., Lampert, C.

(174), Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany, August 2008 (techreport)

Abstract
We study the question of activity classification in videos and present a novel approach for recognizing human action categories in videos by combining information from appearance and motion of human body parts. Our approach uses a tracking step which involves Particle Filtering and a local non - parametric clustering step. The motion information is provided by the trajectory of the cluster modes of a local set of particles. The statistical information about the particles of that cluster over a number of frames provides the appearance information. Later we use a “Bag ofWords” model to build one histogram per video sequence from the set of these robust appearance and motion descriptors. These histograms provide us characteristic information which helps us to discriminate among various human actions and thus classify them correctly. We tested our approach on the standard KTH and Weizmann human action datasets and the results were comparable to the state of the art. Additionally our approach is able to distinguish between activities that involve the motion of complete body from those in which only certain body parts move. In other words, our method discriminates well between activities with “gross motion” like running, jogging etc. and “local motion” like waving, boxing etc.

ei

PDF [BibTex]

PDF [BibTex]


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Example-based Learning for Single-image Super-resolution and JPEG Artifact Removal

Kim, K., Kwon, Y.

(173), Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany, August 2008 (techreport)

Abstract
This paper proposes a framework for single-image super-resolution and JPEG artifact removal. The underlying idea is to learn a map from input low-quality images (suitably preprocessed low-resolution or JPEG encoded images) to target high-quality images based on example pairs of input and output images. To retain the complexity of the resulting learning problem at a moderate level, a patch-based approach is taken such that kernel ridge regression (KRR) scans the input image with a small window (patch) and produces a patchvalued output for each output pixel location. These constitute a set of candidate images each of which reflects different local information. An image output is then obtained as a convex combination of candidates for each pixel based on estimated confidences of candidates. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as it has been done in existing example-based super-resolution algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing super-resolution and JPEG artifact removal methods shows the effectiveness of the proposed method. Furthermore, the proposed method is generic in that it has the potential to be applied to many other image enhancement applications.

ei

PDF [BibTex]

PDF [BibTex]


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Unsupervised Bayesian Time-series Segmentation based on Linear Gaussian State-space Models

Chiappa, S.

(171), Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany, June 2008 (techreport)

Abstract
Unsupervised time-series segmentation in the general scenario in which the number of segment-types and segment boundaries are a priori unknown is a fundamental problem in many applications and requires an accurate segmentation model as well as a way of determining an appropriate number of segment-types. In most approaches, segmentation and determination of number of segment-types are addressed in two separate steps, since the segmentation model assumes a predefined number of segment-types. The determination of number of segment-types is thus achieved by training and comparing several separate models. In this paper, we take a Bayesian approach to a segmentation model based on linear Gaussian state-space models to achieve structure selection within the model. An appropriate prior distribution on the parameters is used to enforce a sparse parametrization, such that the model automatically selects the smallest number of underlying dynamical systems that explain the data well and a parsimonious structure for each dynamical system. As the resulting model is computationally intractable, we introduce a variational approximation, in which a reformulation of the problem enables to use an efficient inference algorithm.

ei

[BibTex]

[BibTex]


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A New Non-monotonic Gradient Projection Method for the Non-negative Least Squares Problem

Kim, D., Sra, S., Dhillon, I.

(TR-08-28), University of Texas, Austin, TX, USA, June 2008 (techreport)

ei

Web [BibTex]

Web [BibTex]


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Non-monotonic Poisson Likelihood Maximization

Sra, S., Kim, D., Schölkopf, B.

(170), Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, June 2008 (techreport)

Abstract
This report summarizes the theory and some main applications of a new non-monotonic algorithm for maximizing a Poisson Likelihood, which for Positron Emission Tomography (PET) is equivalent to minimizing the associated Kullback-Leibler Divergence, and for Transmission Tomography is similar to maximizing the dual of a maximum entropy problem. We call our method non-monotonic maximum likelihood (NMML) and show its application to different problems such as tomography and image restoration. We discuss some theoretical properties such as convergence for our algorithm. Our experimental results indicate that speedups obtained via our non-monotonic methods are substantial.

ei

PDF [BibTex]

PDF [BibTex]