3276 results (BibTeX)

From Humans to Robots and Back: Role of Arm Movement in Medio-lateral Balance Control

Huber, M., Chiovetto, E., Righetti, L., Schaal, S., Giese, M., Sternad, D.

In Proceedings of Dynamic Walking, 2015 (inproceedings)

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[BibTex]

[BibTex]


Full Dynamics LQR Control for Bipedal Walking

Mason, S., Schaal, S., Righetti, L.

Proceedings of Dynamic Walking, 2015 (conference)

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[BibTex]

[BibTex]


Wrong and Useful: Metrics to Assess Simple Walking Models

Rebula, J., Righetti, L., Schaal, S.

In Proceedings of Dynamic Walking, 2015 (inproceedings)

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[BibTex]

[BibTex]


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Scalable Robust Principal Component Analysis using Grassmann Averages

Hauberg, S., Feragen, A., Enficiaud, R., Black, M. J.

IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), December 2015 (article)

Abstract
In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average (GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average (TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.

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preprint pdf from publisher supplemental Project Page [BibTex]

preprint pdf from publisher supplemental Project Page [BibTex]


The effect of frowning on attention

Ibarra Chaoul, A.

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

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[BibTex]

[BibTex]


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

Hohmann, M.

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

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[BibTex]

[BibTex]


Causal Inference in Neuroimaging

Casarsa de Azevedo, L.

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

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[BibTex]

[BibTex]


Sequential Image Deconvolution Using Probabilistic Linear Algebra

Gao, M.

Technical University of Munich, Germany, 2015 (mastersthesis)

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[BibTex]

[BibTex]


Causal inference using invariant prediction: identification and confidence intervals

Peters, J., Bühlmann, P., Meinshausen, N.

Journal of the Royal Statistical Society, Series B, 2015, (with discussion) (article) Accepted

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link (url) [BibTex]

link (url) [BibTex]


A quantum advantage for inferring causal structure

Ried, K., Agnew, M., Vermeyden, L., Janzing, D., Spekkens, R., Resch, K.

Nature Physics, 11(5):414-420, March 2015 (article)

Abstract
The problem of inferring causal relations from observed correlations is relevant to a wide variety of scientific disciplines. Yet given the correlations between just two classical variables, it is impossible to determine whether they arose from a causal influence of one on the other or a common cause influencing both. Only a randomized trial can settle the issue. Here we consider the problem of causal inference for quantum variables. We show that the analogue of a randomized trial, causal tomography, yields a complete solution. We also show that, in contrast to the classical case, one can sometimes infer the causal structure from observations alone. We implement a quantum-optical experiment wherein we control the causal relation between two optical modes, and two measurement schemes—with and without randomization—that extract this relation from the observed correlations. Our results show that entanglement and quantum coherence provide an advantage for causal inference.

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DOI [BibTex]

DOI [BibTex]


Improving Quantitative Susceptibility and R2* Mapping by Applying Retrospective Motion Correction

Feng, X., Loktyushin, A., Deistung, A., Reichenbach, J.

23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

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[BibTex]

[BibTex]


Retrospective rigid motion correction of undersampled MRI data

Loktyushin, A., Babayeva, M., Gallichan, D., Krueger, G., Scheffler, K., Kober, T.

23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

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[BibTex]

[BibTex]


Retrospective motion correction of magnitude-input MR images

Loktyushin, A., Schuler, C., Scheffler, K., Schölkopf, B.

International Conference on Machine Learning (ICML) 2015, Workshop on Machine Learning meets Medical Imaging, 9487, pages: 3-12, Lecture Notes in Computer Science, (Editors: K. K. Bhatia and H. Lombaert), Springer, First International Workshop, MLMMI, July 2015 (conference)

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DOI [BibTex]

DOI [BibTex]


Testing the role of luminance edges in White’s illusion with contour adaptation

Betz, T., Shapley, R., Wichmann, F., Maertens, M.

Journal of Vision, 15(11):1-16, August 2015 (article)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


Noise masking of White’s illusion exposes the weakness of current spatial filtering models of lightness perception

Betz, T., Shapley, R., Wichmann, F., Maertens, M.

Journal of Vision, 15(14):1-17, October 2015 (article)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Method for providing a three dimensional body model (MoSh)

Loper, M., Mahmood, N., Black, M. J.

European Application EP 2899694 and U.S. Patent Application 14/602,701, January 2015 (patent)

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Google Patents MoSh Project [BibTex]

Google Patents MoSh Project [BibTex]


Semi-Supervised Interpolation in an Anticausal Learning Scenario

Janzing, D., Schölkopf, B.

Journal of Machine Learning Research, 16, pages: 1923-1948, September 2015 (article)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Is Breathing Rate a Confounding Variable in Brain-Computer Interfaces (BCIs) Based on EEG Spectral Power?

Ibarra Chaoul, A., Grosse-Wentrup, M.

Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages: 1079-1082, EMBC, August 2015 (conference)

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DOI [BibTex]

DOI [BibTex]


Causal Discovery Beyond Conditional Independences

Sgouritsa, E.

University of Tübingen, Germany, October 2015 (phdthesis)

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[BibTex]

[BibTex]


Diversity of sharp wave-ripples in the CA1 of the macaque hippocampus and their brain wide signatures

Ramirez-Villegas, J., Logothetis, N., Besserve, M.

45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), October 2015 (poster)

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link (url) [BibTex]

link (url) [BibTex]


Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism

Besserve, M.

53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

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[BibTex]

[BibTex]


Independence of cause and mechanism in brain networks

Besserve, M.

DALI workshop on Networks: Processes and Causality, April 2015 (talk)

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[BibTex]

[BibTex]


Diversity of sharp wave-ripple LFP signatures reveals differentiated brain-wide dynamical events

Ramirez-Villegas, J., Logothetis, N., Besserve, M.

Proceedings of the National Academy of Sciences U.S.A, 112(46):E6379-E6387, November 2015 (article)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


Cosmology from Cosmic Shear with DES Science Verification Data

Abbott, T., Abdalla, F., Allam, S., Amara, A., Annis, J., Armstrong, R., Bacon, D., Banerji, M., Bauer, A., Baxter, E., others, O.

arXiv preprint arXiv:1507.05552, 2015 (techreport)

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link (url) [BibTex]

link (url) [BibTex]


Self-calibration of optical lenses

Hirsch, M., Schölkopf, B.

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

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DOI [BibTex]

DOI [BibTex]


The DES Science Verification Weak Lensing Shear Catalogs

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

arXiv preprint arXiv:1507.05603, 2015 (techreport)

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link (url) [BibTex]

link (url) [BibTex]


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)

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[BibTex]

[BibTex]


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, O.

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

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DOI [BibTex]

DOI [BibTex]


Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data

O’Donnell, L., Schultz, T.

In Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pages: 299-319, (Editors: Hotz, I. and Schultz, T.), Springer, 2015 (inbook)

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Project Page [BibTex]

Project Page [BibTex]


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., Schwenzer, N., Pichler, B.

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

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DOI [BibTex]

DOI [BibTex]


Assessment of murine brain tissue shrinkage caused by different histological fixatives using magnetic resonance and computed tomography imaging

Wehrl, H., Bezrukov, I., Wiehr, S., Lehnhoff, M., Fuchs, K., Mannheim, J., Quintanilla-Martinez, L., Kneilling, M., Pichler, B., Sauter, A.

Histology and Histopathology, 30(5):601-613, 2015 (article)

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[BibTex]

[BibTex]


Machine Learning Approaches to Image Deconvolution

Schuler, C.

University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

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[BibTex]

[BibTex]


From Points to Probability Measures: A Statistical Learning on Distributions with Kernel Mean Embedding

Muandet, K.

University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

ei

[BibTex]

[BibTex]


Assessment of tumor heterogeneity using unsupervised graph based clustering of multi-modality imaging data

Katiyar, P., Divine, M., Pichler, B., Disselhorst, J.

European Molecular Imaging Meeting, 2015 (poster)

ei

[BibTex]

[BibTex]


Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model

Divine, M., Harant, M., Katiyar, P., Disselhorst, J., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B.

World Molecular Imaging Conference, 2015 (talk)

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[BibTex]

[BibTex]


Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI

Castaneda, S., Katiyar, P., Russo, F., Disselhorst, J., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B.

World Molecular Imaging Conference, 2015 (talk)

ei

[BibTex]

[BibTex]


Correlation matrix nearness and completion under observation uncertainty

Alaíz, C., Dinuzzo, F., Sra, S.

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

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DOI [BibTex]

DOI [BibTex]


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Probabilistic Line Searches for Stochastic Optimization

Mahsereci, M., Hennig, P.

In Advances in Neural Information Processing Systems 28, pages: 181-189, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (inproceedings)

Abstract
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent. [You can find the matlab research code under `attachments' below. The zip-file contains a minimal working example. The docstring in probLineSearch.m contains additional information. A more polished implementation in C++ will be published here at a later point. For comments and questions about the code please write to mmahsereci@tue.mpg.de.]

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Matlab research code link (url) Project Page [BibTex]

Matlab research code link (url) Project Page [BibTex]


Combined Pose-Wrench and State Machine Representation for Modeling Robotic Assembly Skills

Wahrburg, A., Zeiss, S., Matthias, B., Peters, J., Ding, H.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 852-857, IROS, September 2015 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Stabilizing Novel Objects by Learning to Predict Tactile Slip

Veiga, F., van Hoof, H., Peters, J., Hermans, T.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 5065-5072, IROS, September 2015 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Extracting Low-Dimensional Control Variables for Movement Primitives

Rueckert, E., Mundo, J., Paraschos, A., Peters, J., Neumann, G.

In IEEE International Conference on Robotics and Automation, pages: 1511-1518, ICRA, 2015 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Reinforcement Learning vs Human Programming in Tetherball Robot Games

Parisi, S., Abdulsamad, H., Paraschos, A., Daniel, C., Peters, J.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 6428-6434, IROS, September 2015 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Model-Free Probabilistic Movement Primitives for Physical Interaction

Paraschos, A., Rueckert, E., Peters, J., Neumann, G.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 2860-2866, IROS, September 2015 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives

Manschitz, S., Kober, J., Gienger, M., Peters, J.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 449-455, IROS, September 2015 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


A Probabilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation

Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Peters, J.

In Proceedings of the International Symposium of Robotics Research, ISRR, 2015 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


Semi-Autonomous 3rd-Hand Robot

Lopes, M., Peters, J., Piater, J., Toussaint, M., Baisero, A., Busch, B., Erkent, O., Kroemer, O., Lioutikov, R., Maeda, G., Mollard, Y., Munzer, T., Shukla, D.

In Workshop on Cognitive Robotics in Future Manufacturing Scenarios, European Robotics Forum, 2015 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


Probabilistic Segmentation Applied to an Assembly Task

Lioutikov, R., Neumann, G., Maeda, G., Peters, J.

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 533-540, Humanoids, November 2015 (inproceedings)

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DOI [BibTex]

DOI [BibTex]