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2018


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Parallel and functionally segregated processing of task phase and conscious content in the prefrontal cortex

Kapoor, V., Besserve, M., Logothetis, N. K., Panagiotaropoulos, T. I.

Communications Biology, 1(215):1-12, December 2018 (article)

ei

link (url) DOI [BibTex]

2018


link (url) DOI [BibTex]


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Control of Musculoskeletal Systems using Learned Dynamics Models

Büchler, D., Calandra, R., Schölkopf, B., Peters, J.

IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3161-3168, IEEE, 2018 (article)

Abstract
Controlling musculoskeletal systems, especially robots actuated by pneumatic artificial muscles, is a challenging task due to nonlinearities, hysteresis effects, massive actuator de- lay and unobservable dependencies such as temperature. Despite such difficulties, muscular systems offer many beneficial prop- erties to achieve human-comparable performance in uncertain and fast-changing tasks. For example, muscles are backdrivable and provide variable stiffness while offering high forces to reach high accelerations. In addition, the embodied intelligence deriving from the compliance might reduce the control demands for specific tasks. In this paper, we address the problem of how to accurately control musculoskeletal robots. To address this issue, we propose to learn probabilistic forward dynamics models using Gaussian processes and, subsequently, to employ these models for control. However, Gaussian processes dynamics models cannot be set-up for our musculoskeletal robot as for traditional motor- driven robots because of unclear state composition etc. We hence empirically study and discuss in detail how to tune these approaches to complex musculoskeletal robots and their specific challenges. Moreover, we show that our model can be used to accurately control an antagonistic pair of pneumatic artificial muscles for a trajectory tracking task while considering only one- step-ahead predictions of the forward model and incorporating model uncertainty.

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RAL18final link (url) DOI Project Page [BibTex]

RAL18final link (url) DOI Project Page [BibTex]


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Learning an Approximate Model Predictive Controller with Guarantees

Hertneck, M., Koehler, J., Trimpe, S., Allgöwer, F.

IEEE Control Systems Letters, 2(3):543-548, July 2018 (article)

Abstract
A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction. The framework can be used for a wide class of nonlinear systems. Any standard supervised learning technique (e.g. neural networks) can be employed to approximate the MPC from samples. In order to obtain closed-loop guarantees for the learned MPC, a robust MPC design is combined with statistical learning bounds. The MPC design ensures robustness to inaccurate inputs within given bounds, and Hoeffding’s Inequality is used to validate that the learned MPC satisfies these bounds with high confidence. The result is a closed-loop statistical guarantee on stability and constraint satisfaction for the learned MPC. The proposed learning-based MPC framework is illustrated on a nonlinear benchmark problem, for which we learn a neural network controller with guarantees.

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arXiv PDF DOI [BibTex]

arXiv PDF DOI [BibTex]


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Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

Ruiz, F. J. R., Valera, I., Svensson, L., Perez-Cruz, F.

IEEE Transactions on Cognitive Communications and Networking, 4(2):177-191, June 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Assisting Movement Training and Execution With Visual and Haptic Feedback

Ewerton, M., Rother, D., Weimar, J., Kollegger, G., Wiemeyer, J., Peters, J., Maeda, G.

Frontiers in Neurorobotics, 12, May 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations

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

IEEE Robotics and Automation Letters, 3(2):926-933, April 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Probabilistic movement primitives under unknown system dynamics

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

Advanced Robotics, 32(6):297-310, April 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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An Algorithmic Perspective on Imitation Learning

Osa, T., Pajarinen, J., Neumann, G., Bagnell, J., Abbeel, P., Peters, J.

Foundations and Trends in Robotics, 7(1-2):1-179, March 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Using Probabilistic Movement Primitives in Robotics

Paraschos, A., Daniel, C., Peters, J., Neumann, G.

Autonomous Robots, 42(3):529-551, March 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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A kernel-based approach to learning contact distributions for robot manipulation tasks

Kroemer, O., Leischnig, S., Luettgen, S., Peters, J.

Autonomous Robots, 42(3):581-600, March 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Approximate Value Iteration Based on Numerical Quadrature

Vinogradska, J., Bischoff, B., Peters, J.

IEEE Robotics and Automation Letters, 3(2):1330-1337, January 2018 (article)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Distributed Event-Based State Estimation for Networked Systems: An LMI Approach

Muehlebach, M., Trimpe, S.

IEEE Transactions on Automatic Control, 63(1):269-276, January 2018 (article)

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arXiv (extended version) DOI Project Page [BibTex]

arXiv (extended version) DOI Project Page [BibTex]


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Biomimetic Tactile Sensors and Signal Processing with Spike Trains: A Review

Yi, Z., Zhang, Y., Peters, J.

Sensors and Actuators A: Physical, 269, pages: 41-52, January 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Design and Analysis of the NIPS 2016 Review Process

Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U.

Journal of Machine Learning Research, 19(49):1-34, 2018, *equal contribution (article)

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arXiv link (url) Project Page [BibTex]

arXiv link (url) Project Page [BibTex]


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A Flexible Approach for Fair Classification

Zafar, M. B., Valera, I., Gomez Rodriguez, M., Gummadi, K.

Journal of Machine Learning, 2018 (article) Accepted

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Project Page [BibTex]

Project Page [BibTex]


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Adaptation and Robust Learning of Probabilistic Movement Primitives

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.

IEEE Transactions on Robotics, 2018 (article) In revision

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arXiv [BibTex]

arXiv [BibTex]


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Does universal controllability of physical systems prohibit thermodynamic cycles?

Janzing, D., Wocjan, P.

Open Systems and Information Dynamics, 25(3):1850016, 2018 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Learning Causality and Causality-Related Learning: Some Recent Progress

Zhang, K., Schölkopf, B., Spirtes, P., Glymour, C.

National Science Review, 5(1):26-29, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Online optimal trajectory generation for robot table tennis

Koc, O., Maeda, G., Peters, J.

Robotics and Autonomous Systems, 105, pages: 121-137, 2018 (article)

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PDF link (url) DOI [BibTex]

PDF link (url) DOI [BibTex]


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Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S.

Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

Abstract
This paper introduces a novel Hilbert space representation of a counterfactual distribution---called counterfactual mean embedding (CME)---with applications in nonparametric causal inference. Counterfactual prediction has become an ubiquitous tool in machine learning applications, such as online advertisement, recommendation systems, and medical diagnosis, whose performance relies on certain interventions. To infer the outcomes of such interventions, we propose to embed the associated counterfactual distribution into a reproducing kernel Hilbert space (RKHS) endowed with a positive definite kernel. Under appropriate assumptions, the CME allows us to perform causal inference over the entire landscape of the counterfactual distribution. The CME can be estimated consistently from observational data without requiring any parametric assumption about the underlying distributions. We also derive a rate of convergence which depends on the smoothness of the conditional mean and the Radon-Nikodym derivative of the underlying marginal distributions. Our framework can deal with not only real-valued outcome, but potentially also more complex and structured outcomes such as images, sequences, and graphs. Lastly, our experimental results on off-policy evaluation tasks demonstrate the advantages of the proposed estimator.

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arXiv [BibTex]

arXiv [BibTex]


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Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions

Osa, T., Peters, J., Neumann, G.

Advanced Robotics, 32(18):955-968, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Autofocusing-based phase correction

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.

Magnetic Resonance in Medicine, 80(3):958-968, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Case series: Slowing alpha rhythm in late-stage ALS patients

Hohmann, M. R., Fomina, T., Jayaram, V., Emde, T., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

Clinical Neurophysiology, 129(2):406-408, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling

Šošić, A., Rueckert, E., Peters, J., Zoubir, A., Koeppl, H.

Journal of Machine Learning Research, 19(69):1-45, 2018 (article)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Grip Stabilization of Novel Objects using Slip Prediction

Veiga, F., Peters, J., Hermans, T.

IEEE Transactions on Haptics, 2018 (article) In press

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DOI [BibTex]

DOI [BibTex]


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Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis—implications for brain–computer interfacing

Kellmeyer, P., Grosse-Wentrup, M., Schulze-Bonhage, A., Ziemann, U., Ball, T.

Journal of Neural Engineering, 15(4):041003, IOP Publishing, 2018 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


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Kernel-based tests for joint independence

Pfister, N., Bühlmann, P., Schölkopf, B., Peters, J.

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(1):5-31, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test

Babbar, R., Heni, M., Peter, A., Hrabě de Angelis, M., Häring, H., Fritsche, A., Preissl, H., Schölkopf, B., Wagner, R.

Frontiers in Endocrinology, 9, pages: 82, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Invariant Models for Causal Transfer Learning

Rojas-Carulla, M., Schölkopf, B., Turner, R., Peters, J.

Journal of Machine Learning Research, 19(36):1-34, 2018 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


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MOABB: Trustworthy algorithm benchmarking for BCIs

Jayaram, V., Barachant, A.

Journal of Neural Engineering, 15(6):066011, 2018 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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f-Divergence constrained policy improvement

Belousov, B., Peters, J.

Journal of Machine Learning Research, 2018 (article) Submitted

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Project Page [BibTex]

Project Page [BibTex]


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Phylogenetic convolutional neural networks in metagenomics

Fioravanti*, D., Giarratano*, Y., Maggio*, V., Agostinelli, C., Chierici, M., Jurman, G., Furlanello, C.

BMC Bioinformatics, 19(2):49 pages, 2018, *equal contribution (article)

ei

DOI [BibTex]

DOI [BibTex]


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Food specific inhibitory control under negative mood in binge-eating disorder: Evidence from a multimethod approach

Leehr, E. J., Schag, K., Dresler, T., Grosse-Wentrup, M., Hautzinger, M., Fallgatter, A. J., Zipfel, S., Giel, K. E., Ehlis, A.

International Journal of Eating Disorders, 51(2):112-123, Wiley Online Library, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Linking imaging to omics utilizing image-guided tissue extraction

Disselhorst, J. A., Krueger, M. A., Ud-Dean, S. M. M., Bezrukov, I., Jarboui, M. A., Trautwein, C., Traube, A., Spindler, C., Cotton, J. M., Leibfritz, D., Pichler, B. J.

Proceedings of the National Academy of Sciences, 115(13):E2980-E2987, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Discriminative Transfer Learning for General Image Restoration

Xiao, L., Heide, F., Heidrich, W., Schölkopf, B., Hirsch, M.

IEEE Transactions on Image Processing, 27(8):4091-4104, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Dissecting the synapse- and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples

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

Neuron, 100(5):1224-1240, 2018 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Optimizing Execution of Dynamic Goal-Directed Robot Movements with Learning Control

Koc, O., Maeda, G., Peters, J.

IEEE Transactions on Robotics, 2018 (article) Submitted

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arXiv [BibTex]

arXiv [BibTex]


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In-Hand Object Stabilization by Independent Finger Control

Veiga, F. F., Edin, B. B., Peters, J.

IEEE Transactions on Robotics, 2018 (article) Submitted

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[BibTex]

[BibTex]


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Visualizing and understanding Sum-Product Networks

Vergari, A., Di Mauro, N., Esposito, F.

Machine Learning, 2018 (article)

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DOI [BibTex]

DOI [BibTex]


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Learning to serve: an experimental study for a new learning from demonstrations framework

Koc, O., Peters, J.

IEEE Robotics and Automation Letters (ICRA/RA-L), 2018 (article) Accepted

ei

[BibTex]

[BibTex]


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Non-Equilibrium Relations for Bounded Rational Decision-Making in Changing Environments

Grau-Moya, J, Krüger, M, Braun, DA

Entropy, 20(1:1):1-28, January 2018 (article)

Abstract
Living organisms from single cells to humans need to adapt continuously to respond to changes in their environment. The process of behavioural adaptation can be thought of as improving decision-making performance according to some utility function. Here, we consider an abstract model of organisms as decision-makers with limited information-processing resources that trade off between maximization of utility and computational costs measured by a relative entropy, in a similar fashion to thermodynamic systems undergoing isothermal transformations. Such systems minimize the free energy to reach equilibrium states that balance internal energy and entropic cost. When there is a fast change in the environment, these systems evolve in a non-equilibrium fashion because they are unable to follow the path of equilibrium distributions. Here, we apply concepts from non-equilibrium thermodynamics to characterize decision-makers that adapt to changing environments under the assumption that the temporal evolution of the utility function is externally driven and does not depend on the decision-maker’s action. This allows one to quantify performance loss due to imperfect adaptation in a general manner and, additionally, to find relations for decision-making similar to Crooks’ fluctuation theorem and Jarzynski’s equality. We provide simulations of several exemplary decision and inference problems in the discrete and continuous domains to illustrate the new relations.

ei

DOI [BibTex]

DOI [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)

ei

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)

ei

DOI Project Page [BibTex]

DOI Project Page [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 Project Page [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 Project Page [BibTex]

DOI Project Page [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)

ei

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 Project Page [BibTex]

DOI Project Page [BibTex]