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2015


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Correlation matrix nearness and completion under observation uncertainty

Alaíz, C. M., Dinuzzo, F., Sra, S.

IMA Journal of Numerical Analysis, 35(1):325-340, 2015 (article)

DOI [BibTex]

2015

DOI [BibTex]


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Quantitative evaluation of segmentation- and atlas- based attenuation correction for PET/MR on pediatric patients

Bezrukov, I., Schmidt, H., Gatidis, S., Mantlik, F., Schäfer, J. F., Schwenzer, N., Pichler, B. J.

Journal of Nuclear Medicine, 56(7):1067-1074, 2015 (article)

DOI [BibTex]

DOI [BibTex]


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Self-calibration of optical lenses

Hirsch, M., Schölkopf, B.

In IEEE International Conference on Computer Vision (ICCV 2015), pages: 612-620, IEEE, 2015 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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The DES Science Verification Weak Lensing Shear Catalogs

Jarvis, M., Sheldon, E., Zuntz, J., Kacprzak, T., Bridle, S. L., Amara, A., Armstrong, R., Becker, M. R., Bernstein, G. M., Bonnett, C., others,

arXiv preprint arXiv:1507.05603, 2015 (techreport)

link (url) [BibTex]

link (url) [BibTex]


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Sequential Image Deconvolution Using Probabilistic Linear Algebra

Gao, M.

Technical University of Munich, Germany, 2015 (mastersthesis)

[BibTex]

[BibTex]


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Telling cause from effect in deterministic linear dynamical systems

Shajarisales, N., Janzing, D., Schölkopf, B., Besserve, M.

In Proceedings of the 32nd International Conference on Machine Learning, 37, pages: 285–294, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Hohmann, M. R., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Müller vom Hagen, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, pages: 3187-3191, SMC, 2015 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Probabilistic numerics and uncertainty in computations

Hennig, P., Osborne, M. A., Girolami, M.

Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 471(2179), 2015 (article)

Abstract
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Efficient Learning of Linear Separators under Bounded Noise

Awasthi, P., Balcan, M., Haghtalab, N., Urner, R.

In Proceedings of the 28th Conference on Learning Theory, 40, pages: 167-190, (Editors: Grünwald, P. and Hazan, E. and Kale, S.), JMLR, COLT, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Learning multiple collaborative tasks with a mixture of Interaction Primitives

Ewerton, M., Neumann, G., Lioutikov, R., Ben Amor, H., Peters, J., Maeda, G.

In IEEE International Conference on Robotics and Automation, pages: 1535-1542, ICRA, 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Disparity estimation from a generative light field model

Köhler, R., Schölkopf, B., Hirsch, M.

IEEE International Conference on Computer Vision (ICCV 2015), Workshop on Inverse Rendering, 2015, Note: This work has been presented as a poster and is not included in the workshop proceedings. (poster)

[BibTex]

[BibTex]


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Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data

Melchior, P., Suchyta, E., Huff, E., Hirsch, M., Kacprzak, T., Rykoff, E., Gruen, D., Armstrong, R., Bacon, D., Bechtol, K., others,

Monthly Notices of the Royal Astronomical Society, 449(3):2219-2238, Oxford University Press, 2015 (article)

DOI [BibTex]

DOI [BibTex]


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Causal Inference in Neuroimaging

Casarsa de Azevedo, L.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

[BibTex]

[BibTex]


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The effect of frowning on attention

Ibarra Chaoul, A.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 (mastersthesis)

[BibTex]

[BibTex]


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Justifying Information-Geometric Causal Inference

Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B.

In Measures of Complexity: Festschrift for Alexey Chervonenkis, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)

DOI [BibTex]

DOI [BibTex]


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Subspace Alignement based Domain Adaptation for RCNN detector

Raj, A., V., N., Tuytelaars, T.

Proceedings of the 26th British Machine Vision Conference (BMVC 2015), pages: 166.1-166.11, (Editors: Xianghua Xie and Mark W. Jones and Gary K. L. Tam), 2015 (conference)

DOI [BibTex]

DOI [BibTex]


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Practical Probabilistic Programming with Monads

Ścibior, A., Ghahramani, Z., Gordon, A. D.

Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell, pages: 165-176, Haskell ’15, ACM, 2015 (conference)

DOI [BibTex]

DOI [BibTex]


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The search for single exoplanet transits in the Kepler light curves

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

link (url) [BibTex]

link (url) [BibTex]


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Entropic Movement Complexity Reflects Subjective Creativity Rankings of Visualized Hand Motion Trajectories

Peng, Z, Braun, DA

Frontiers in Psychology, 6(1879):1-13, December 2015 (article)

Abstract
In a previous study we have shown that human motion trajectories can be characterized by translating continuous trajectories into symbol sequences with well-defined complexity measures. Here we test the hypothesis that the motion complexity individuals generate in their movements might be correlated to the degree of creativity assigned by a human observer to the visualized motion trajectories. We asked participants to generate 55 novel hand movement patterns in virtual reality, where each pattern had to be repeated 10 times in a row to ensure reproducibility. This allowed us to estimate a probability distribution over trajectories for each pattern. We assessed motion complexity not only by the previously proposed complexity measures on symbolic sequences, but we also propose two novel complexity measures that can be directly applied to the distributions over trajectories based on the frameworks of Gaussian Processes and Probabilistic Movement Primitives. In contrast to previous studies, these new methods allow computing complexities of individual motion patterns from very few sample trajectories. We compared the different complexity measures to how a group of independent jurors rank ordered the recorded motion trajectories according to their personal creativity judgment. We found three entropic complexity measures that correlate significantly with human creativity judgment and discuss differences between the measures. We also test whether these complexity measures correlate with individual creativity in divergent thinking tasks, but do not find any consistent correlation. Our results suggest that entropic complexity measures of hand motion may reveal domain-specific individual differences in kinesthetic creativity.

DOI [BibTex]

DOI [BibTex]


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Bounded rationality, abstraction and hierarchical decision-making: an information-theoretic optimality principle

Genewein, T, Leibfried, F, Grau-Moya, J, Braun, DA

Frontiers in Robotics and AI, 2(27):1-24, October 2015 (article)

Abstract
Abstraction and hierarchical information-processing are hallmarks of human and animal intelligence underlying the unrivaled flexibility of behavior in biological systems. Achieving such a flexibility in artificial systems is challenging, even with more and more computational power. Here we investigate the hypothesis that abstraction and hierarchical information-processing might in fact be the consequence of limitations in information-processing power. In particular, we study an information-theoretic framework of bounded rational decision-making that trades off utility maximization against information-processing costs. We apply the basic principle of this framework to perception-action systems with multiple information-processing nodes and derive bounded optimal solutions. We show how the formation of abstractions and decision-making hierarchies depends on information-processing costs. We illustrate the theoretical ideas with example simulations and conclude by formalizing a mathematically unifying optimization principle that could potentially be extended to more complex systems.

DOI [BibTex]

DOI [BibTex]


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Developing neural networks with neurons competing for survival

Peng, Z, Braun, DA

pages: 152-153, IEEE, Piscataway, NJ, USA, 5th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (IEEE ICDL-EPIROB), August 2015 (conference)

Abstract
We study developmental growth in a feedforward neural network model inspired by the survival principle in nature. Each neuron has to select its incoming connections in a way that allow it to fire, as neurons that are not able to fire over a period of time degenerate and die. In order to survive, neurons have to find reoccurring patterns in the activity of the neurons in the preceding layer, because each neuron requires more than one active input at any one time to have enough activation for firing. The sensory input at the lowest layer therefore provides the maximum amount of activation that all neurons compete for. The whole network grows dynamically over time depending on how many patterns can be found and how many neurons can maintain themselves accordingly. We show in simulations that this naturally leads to abstractions in higher layers that emerge in a unsupervised fashion. When evaluating the network in a supervised learning paradigm, it is clear that our network is not competitive. What is interesting though is that this performance was achieved by neurons that simply struggle for survival and do not know about performance error. In contrast to most studies on neural evolution that rely on a network-wide fitness function, our goal was to show that learning behaviour can appear in a system without being driven by any specific utility function or reward signal.

DOI [BibTex]

DOI [BibTex]


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Signaling equilibria in sensorimotor interactions

Leibfried, F, Grau-Moya, J, Braun, DA

Cognition, 141, pages: 73-86, August 2015 (article)

Abstract
Although complex forms of communication like human language are often assumed to have evolved out of more simple forms of sensorimotor signaling, less attention has been devoted to investigate the latter. Here, we study communicative sensorimotor behavior of humans in a two-person joint motor task where each player controls one dimension of a planar motion. We designed this joint task as a game where one player (the sender) possesses private information about a hidden target the other player (the receiver) wants to know about, and where the sender's actions are costly signals that influence the receiver's control strategy. We developed a game-theoretic model within the framework of signaling games to investigate whether subjects' behavior could be adequately described by the corresponding equilibrium solutions. The model predicts both separating and pooling equilibria, in which signaling does and does not occur respectively. We observed both kinds of equilibria in subjects and found that, in line with model predictions, the propensity of signaling decreased with increasing signaling costs and decreasing uncertainty on the part of the receiver. Our study demonstrates that signaling games, which have previously been applied to economic decision-making and animal communication, provide a framework for human signaling behavior arising during sensorimotor interactions in continuous and dynamic environments.

DOI [BibTex]

DOI [BibTex]


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Structure Learning in Bayesian Sensorimotor Integration

Genewein, T, Hez, E, Razzaghpanah, Z, Braun, DA

PLoS Computational Biology, 11(8):1-27, August 2015 (article)

Abstract
Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration.

DOI [BibTex]

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.

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.

DOI [BibTex]

DOI [BibTex]

1995


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View-based cognitive map learning by an autonomous robot

Mallot, H., Bülthoff, H., Georg, P., Schölkopf, B., Yasuhara, K.

In Proceedings International Conference on Artificial Neural Networks, vol. 2, pages: 381-386, (Editors: Fogelman-Soulié, F.), EC2, Paris, France, Conférence Internationale sur les Réseaux de Neurones Artificiels (ICANN '95), October 1995 (inproceedings)

Abstract
This paper presents a view-based approach to map learning and navigation in mazes. By means of graph theory we have shown that the view-graph is a sufficient representation for map behaviour such as path planning. A neural network for unsupervised learning of the view-graph from sequences of views is constructed. We use a modified Kohonen (1988) learning rule that transforms temporal sequence (rather than featural similarity) into connectedness. In the main part of the paper, we present a robot implementation of the scheme. The results show that the proposed network is able to support map behaviour in simple environments.

PDF [BibTex]

1995

PDF [BibTex]


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Extracting support data for a given task

Schölkopf, B., Burges, C., Vapnik, V.

In First International Conference on Knowledge Discovery & Data Mining (KDD-95), pages: 252-257, (Editors: UM Fayyad and R Uthurusamy), AAAI Press, Menlo Park, CA, USA, August 1995 (inproceedings)

Abstract
We report a novel possibility for extracting a small subset of a data base which contains all the information necessary to solve a given classification task: using the Support Vector Algorithm to train three different types of handwritten digit classifiers, we observed that these types of classifiers construct their decision surface from strongly overlapping small (k: 4%) subsets of the data base. This finding opens up the possibiiity of compressing data bases significantly by disposing of the data which is not important for the solution of a given task. In addition, we show that the theory allows us to predict the classifier that will have the best generalization ability, based solely on performance on the training set and characteristics of the learning machines. This finding is important for cases where the amount of available data is limited.

PDF [BibTex]

PDF [BibTex]


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View-Based Cognitive Mapping and Path Planning

Schölkopf, B., Mallot, H.

Adaptive Behavior, 3(3):311-348, January 1995 (article)

Abstract
This article presents a scheme for learning a cognitive map of a maze from a sequence of views and movement decisions. The scheme is based on an intermediate representation called the view graph, whose nodes correspond to the views whereas the labeled edges represent the movements leading from one view to another. By means of a graph theoretical reconstruction method, the view graph is shown to carry complete information on the topological and directional structure of the maze. Path planning can be carried out directly in the view graph without actually performing this reconstruction. A neural network is presented that learns the view graph during a random exploration of the maze. It is based on an unsupervised competitive learning rule translating temporal sequence (rather than similarity) of views into connectedness in the network. The network uses its knowledge of the topological and directional structure of the maze to generate expectations about which views are likely to be encountered next, improving the view-recognition performance. Numerical simulations illustrate the network's ability for path planning and the recognition of views degraded by random noise. The results are compared to findings of behavioral neuroscience.

Web DOI [BibTex]

Web DOI [BibTex]


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Suppression and creation of chaos in a periodically forced Lorenz system.

Franz, MO., Zhang, MH.

Physical Review, E 52, pages: 3558-3565, 1995 (article)

Abstract
Periodic forcing is introduced into the Lorenz model to study the effects of time-dependent forcing on the behavior of the system. Such a nonautonomous system stays dissipative and has a bounded attracting set which all trajectories finally enter. The possible kinds of attracting sets are restricted to periodic orbits and strange attractors. A large-scale survey of parameter space shows that periodic forcing has mainly three effects in the Lorenz system depending on the forcing frequency: (i) Fixed points are replaced by oscillations around them; (ii) resonant periodic orbits are created both in the stable and the chaotic region; (iii) chaos is created in the stable region near the resonance frequency and in periodic windows. A comparison to other studies shows that part of this behavior has been observed in simulations of higher truncations and real world experiments. Since very small modulations can already have a considerable effect, this suggests that periodic processes such as annual or diurnal cycles should not be omitted even in simple climate models.

[BibTex]

[BibTex]


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A New Method for Constructing Artificial Neural Networks

Vapnik, V., Burges, C., Schölkopf, B.

AT & T Bell Laboratories, 1995 (techreport)

[BibTex]

[BibTex]


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Image segmentation from motion: just the loss of high-spatial-frequency content ?

Wichmann, F., Henning, G.

Perception, 24, pages: S19, 1995 (poster)

Abstract
The human contrast sensitivity function (CSF) is bandpass for stimuli of low temporal frequency but, for moving stimuli, results in a low-pass CSF with large high spatial-frequency losses. Thus the high spatial-frequency content of images moving on the retina cannot be seen; motion perception could be facilitated by, or even be based on, the selective loss of high spatial-frequency content. 2-AFC image segmentation experiments were conducted with segmentation based on motion or on form. In the latter condition, the form difference mirrored that produced by moving stimuli. This was accomplished by generating stimulus elements which were spectrally either broadband or low-pass. For the motion used, the spectral difference between static broadband and static low-pass elements matched the spectral difference between moving and static broadband elements. On the hypothesis that segmentation from motion is based on the detection of regions devoid of high spatial-frequencies, both tasks should be similarly difficult for human observers. However, neither image segmentation (nor, incidentally, motion detection) was sensitive to the high spatial-frequency content of the stimuli. Thus changes in perceptual form produced by moving stimuli appear not to be used as a cue for image segmentation.

[BibTex]