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2019


Semi-supervised learning, causality, and the conditional cluster assumption
Semi-supervised learning, causality, and the conditional cluster assumption

von Kügelgen, J., Mey, A., Loog, M., Schölkopf, B.

NeurIPS 2019 Workshop “Do the right thing”: machine learning and causal inference for improved decision making, December 2019 (poster)

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Poster PDF link (url) [BibTex]

2019


Poster PDF link (url) [BibTex]


Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks

von Kügelgen, J., Rubenstein, P. K., Schölkopf, B., Weller, A.

NeurIPS 2019 Workshop “Do the right thing”: machine learning and causal inference for improved decision making, December 2019 (poster)

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arXiv Poster link (url) [BibTex]

arXiv Poster link (url) [BibTex]


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Dynamics of beneficial epidemics

Berdahl, A., Brelsford, C., De Bacco, C., Dumas, M., Ferdinand, V., Grochow, J. A., nt Hébert-Dufresne, L., Kallus, Y., Kempes, C. P., Kolchinsky, A., Larremore, D. B., Libby, E., Power, E. A., A., S. C., Tracey, B. D.

Scientific Reports, 9, pages: 15093, October 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Convolutional neural networks: A magic bullet for gravitational-wave detection?

Gebhard, T., Kilbertus, N., Harry, I., Schölkopf, B.

Physical Review D, 100(6):063015, American Physical Society, September 2019 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Data scarcity, robustness and extreme multi-label classification

Babbar, R., Schölkopf, B.

Machine Learning, 108(8):1329-1351, September 2019, Special Issue of the ECML PKDD 2019 Journal Track (article)

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DOI [BibTex]

DOI [BibTex]


Learning Variable Impedance Control for Contact Sensitive Tasks
Learning Variable Impedance Control for Contact Sensitive Tasks

Bogdanovic, M., Khadiv, M., Righetti, L.

arXiv preprint, arXiv:1907.07500, July 2019 (article)

Abstract
Reinforcement learning algorithms have shown great success in solving different problems ranging from playing video games to robotics. However, they struggle to solve delicate robotic problems, especially those involving contact interactions. Though in principle a policy outputting joint torques should be able to learn these tasks, in practice we see that they have difficulty to robustly solve the problem without any structure in the action space. In this paper, we investigate how the choice of action space can give robust performance in presence of contact uncertainties. We propose to learn a policy that outputs impedance and desired position in joint space as a function of system states without imposing any other structure to the problem. We compare the performance of this approach to torque and position control policies under different contact uncertainties. Extensive simulation results on two different systems, a hopper (floating-base) with intermittent contacts and a manipulator (fixed-base) wiping a table, show that our proposed approach outperforms policies outputting torque or position in terms of both learning rate and robustness to environment uncertainty.

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[BibTex]


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SPINDLE: End-to-end learning from EEG/EMG to extrapolate animal sleep scoring across experimental settings, labs and species

Miladinovic, D., Muheim, C., Bauer, S., Spinnler, A., Noain, D., Bandarabadi, M., Gallusser, B., Krummenacher, G., Baumann, C., Adamantidis, A., Brown, S. A., Buhmann, J. M.

PLOS Computational Biology, 15(4):1-30, Public Library of Science, April 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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A 32-channel multi-coil setup optimized for human brain shimming at 9.4T

Aghaeifar, A., Zhou, J., Heule, R., Tabibian, B., Schölkopf, B., Jia, F., Zaitsev, M., Scheffler, K.

Magnetic Resonance in Medicine, 2019, (Early View) (article)

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DOI [BibTex]

DOI [BibTex]


Multidimensional Contrast Limited Adaptive Histogram Equalization
Multidimensional Contrast Limited Adaptive Histogram Equalization

Stimper, V., Bauer, S., Ernstorfer, R., Schölkopf, B., Xian, R. P.

IEEE Access, 7, pages: 165437-165447, 2019 (article)

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arXiv link (url) DOI [BibTex]

arXiv link (url) DOI [BibTex]


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TD-regularized actor-critic methods

Parisi, S., Tangkaratt, V., Peters, J., Khan, M. E.

Machine Learning, 108(8):1467-1501, (Editors: Karsten Borgwardt, Po-Ling Loh, Evimaria Terzi, and Antti Ukkonen), 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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A Robustness Analysis of Inverse Optimal Control of Bipedal Walking

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

IEEE Robotics and Automation Letters, 4(4):4531-4538, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective

Tronarp, F., Kersting, H., Särkkä, S. H. P.

Statistics and Computing, 29(6):1297-1315, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots

Büchler, D., Calandra, R., Peters, J.

2019 (article) Submitted

Abstract
High-speed and high-acceleration movements are inherently hard to control. Applying learning to the control of such motions on anthropomorphic robot arms can improve the accuracy of the control but might damage the system. The inherent exploration of learning approaches can lead to instabilities and the robot reaching joint limits at high speeds. Having hardware that enables safe exploration of high-speed and high-acceleration movements is therefore desirable. To address this issue, we propose to use robots actuated by Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees of freedom (DoFs) robot arm that reaches high joint angle accelerations of up to 28000 °/s^2 while avoiding dangerous joint limits thanks to the antagonistic actuation and limits on the air pressure ranges. With this robot arm, we are able to tune control parameters using Bayesian optimization directly on the hardware without additional safety considerations. The achieved tracking performance on a fast trajectory exceeds previous results on comparable PAM-driven robots. We also show that our system can be controlled well on slow trajectories with PID controllers due to careful construction considerations such as minimal bending of cables, lightweight kinematics and minimal contact between PAMs and PAMs with the links. Finally, we propose a novel technique to control the the co-contraction of antagonistic muscle pairs. Experimental results illustrate that choosing the optimal co-contraction level is vital to reach better tracking performance. Through the use of PAM-driven robots and learning, we do a small step towards the future development of robots capable of more human-like motions.

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Arxiv Video [BibTex]


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Perception of temporal dependencies in autoregressive motion

Meding, K., Schölkopf, B., Wichmann, F. A.

European Conference on Visual Perception (ECVP), 2019 (poster)

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[BibTex]

[BibTex]


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Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

Pfister*, N., Weichwald*, S., Bühlmann, P., Schölkopf, B.

Journal of Machine Learning Research, 20(147):1-50, 2019, *equal contribution (article)

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ArXiv Code Project page PDF link (url) Project Page Project Page [BibTex]

ArXiv Code Project page PDF link (url) Project Page Project Page [BibTex]


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Enhancing Human Learning via Spaced Repetition Optimization

Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the National Academy of Sciences, 116(10):3988-3993, National Academy of Sciences, 2019 (article)

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link (url) DOI Project Page Project Page [BibTex]

link (url) DOI Project Page Project Page [BibTex]


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Entropic Regularization of Markov Decision Processes

Belousov, B., Peters, J.

Entropy, 21(7):674, 2019 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Searchers adjust their eye-movement dynamics to target characteristics in natural scenes

Rothkegel, L., Schütt, H., Trukenbrod, H., Wichmann, F. A., Engbert, R.

Scientific Reports, 9(1635), 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Spatial statistics for gaze patterns in scene viewing: Effects of repeated viewing

Trukenbrod, H. A., Barthelmé, S., Wichmann, F. A., Engbert, R.

Journal of Vision, 19(6):19, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Rigid vs compliant contact: an experimental study on biped walking

Khadiv, M., Moosavian, S. A. A., Yousefi-Koma, A., Sadedel, M., Ehsani-Seresht, A., Mansouri, S.

Multibody System Dynamics, 45(4):379-401, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Quantum mean embedding of probability distributions

Kübler, J. M., Muandet, K., Schölkopf, B.

Physical Review Research, 1(3):033159, American Physical Society, 2019 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Co-Contraction facilitates Body Stiffness Modulation during Swimming with Sensory Feedback in a Soft Biorobotic Physical Model

Jusufi, A., Vogt, D., Wood, R. J.

Integrative and Comparative Biology, 59(Supplement 1):E116-E116, Society of Integrative and Comparative Biology, McLean, VA, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Inferring causation from time series with perspectives in Earth system sciences

Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M., Munoz-Mari, J., van Nes, E., Peters, J., Quax, R., Reichstein, M., Scheffer, M., Schölkopf, B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., Zscheischler, J.

Nature Communications, 10(2553), 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Analysis of cause-effect inference by comparing regression errors

Blöbaum, P., Janzing, D., Washio, T., Shimizu, S., Schölkopf, B.

PeerJ Computer Science, 5, pages: e169, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Learning Intention Aware Online Adaptation of Movement Primitives

Koert, D., Pajarinen, J., Schotschneider, A., Trick, S., Rothkopf, C., Peters, J.

IEEE Robotics and Automation Letters, 4(4):3719-3726, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Spread-spectrum magnetic resonance imaging

Scheffler, K., Loktyushin, A., Bause, J., Aghaeifar, A., Steffen, T., Schölkopf, B.

Magnetic Resonance in Medicine, 82(3):877-885, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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How Cognitive Models of Human Body Experience Might Push Robotics

Schürmann, T., Mohler, B. J., Peters, J., Beckerle, P.

Frontiers in Neurorobotics, 13(14), 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Phenomenal Causality and Sensory Realism

Bruijns, S. A., Meding, K., Schölkopf, B., Wichmann, F. A.

European Conference on Visual Perception (ECVP), 2019 (poster)

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[BibTex]

[BibTex]


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Dense connectomic reconstruction in layer 4 of the somatosensory cortex

Motta, A., Berning, M., Boergens, K. M., Staffler, B., Beining, M., Loomba, S., Hennig, P., Wissler, H., Helmstaedter, M.

Science, 366(6469):eaay3134, American Association for the Advancement of Science, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Learning Trajectory Distributions for Assisted Teleoperation and Path Planning

Ewerton, M., Arenz, O., Maeda, G., Koert, D., Kolev, Z., Takahashi, M., Peters, J.

Frontiers in Robotics and AI, 6, pages: 89, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Birch tar production does not prove Neanderthal behavioral complexity

Schmidt, P., Blessing, M., Rageot, M., Iovita, R., Pfleging, J., Nickel, K. G., Righetti, L., Tennie, C.

Proceedings of the National Academy of Sciences (PNAS), 116(36):17707-17711, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Brainglance: Visualizing Group Level MRI Data at One Glance

Stelzer, J., Lacosse, E., Bause, J., Scheffler, K., Lohmann, G.

Frontiers in Neuroscience, 13(972), 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces

Klus, S., Schuster, I., Muandet, K.

Journal of Nonlinear Science, 2019, First Online: 21 August 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Workshops of the seventh international brain-computer interface meeting: not getting lost in translation

Huggins, J. E., Guger, C., Aarnoutse, E., Allison, B., Anderson, C. W., Bedrick, S., Besio, W., Chavarriaga, R., Collinger, J. L., Do, A. H., Herff, C., Hohmann, M., Kinsella, M., Lee, K., Lotte, F., Müller-Putz, G., Nijholt, A., Pels, E., Peters, B., Putze, F., Rupp, R. S. G., Scott, S., Tangermann, M., Tubig, P., Zander, T.

Brain-Computer Interfaces, 6(3):71-101, Taylor & Francis, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Compatible natural gradient policy search

Pajarinen, J., Thai, H. L., Akrour, R., Peters, J., Neumann, G.

Machine Learning, 108(8):1443-1466, (Editors: Karsten Borgwardt, Po-Ling Loh, Evimaria Terzi, and Antti Ukkonen), 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Learning stable and predictive structures in kinetic systems

Pfister, N., Bauer, S., Peters, J.

Proceedings of the National Academy of Sciences (PNAS), 116(51):25405-25411, 2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Fairness Constraints: A Flexible Approach for Fair Classification

Zafar, M. B., Valera, I., Gomez-Rodriguez, M., Krishna, P.

Journal of Machine Learning Research, 20(75):1-42, 2019 (article)

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link (url) [BibTex]

link (url) [BibTex]

2015


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Quantifying changes in climate variability and extremes: Pitfalls and their overcoming

Sippel, S., Zscheischler, J., Heimann, M., Otto, F. E. L., Peters, J., Mahecha, M. D.

Geophysical Research Letters, 42(22):9990-9998, November 2015 (article)

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DOI [BibTex]

2015


DOI [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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

Ramirez-Villegas, J. F., Logothetis, N. K., 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]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer

Besserve, M., Lowe, S. C., Logothetis, N. K., Schölkopf, B., Panzeri, S.

PLOS Biology, 13(9):e1002257, September 2015 (article)

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DOI [BibTex]

DOI [BibTex]


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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) [BibTex]

link (url) [BibTex]


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

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

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

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DOI [BibTex]

DOI [BibTex]


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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]


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

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

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

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[BibTex]

[BibTex]


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Blind multirigid retrospective motion correction of MR images

Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B.

Magnetic Resonance in Medicine, 73(4):1457-1468, April 2015 (article)

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DOI [BibTex]

DOI [BibTex]