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2017


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Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

Gu, S., Lillicrap, T., Turner, R. E., Ghahramani, Z., Schölkopf, B., Levine, S.

Advances in Neural Information Processing Systems 30, pages: 3849-3858, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

2017


link (url) Project Page [BibTex]


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Boosting Variational Inference: an Optimization Perspective

Locatello, F., Khanna, R., Ghosh, J., Rätsch, G.

Workshop: Advances in Approximate Bayesian Inference at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Learning Independent Causal Mechanisms

Parascandolo, G., Rojas-Carulla, M., Kilbertus, N., Schölkopf, B.

Workshop: Learning Disentangled Representations: from Perception to Control at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Avoiding Discrimination through Causal Reasoning

Kilbertus, N., Rojas-Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., Schölkopf, B.

Advances in Neural Information Processing Systems 30, pages: 656-666, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees

Locatello, F., Tschannen, M., Rätsch, G., Jaggi, M.

Advances in Neural Information Processing Systems 30, pages: 773-784, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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AdaGAN: Boosting Generative Models

Tolstikhin, I., Gelly, S., Bousquet, O., Simon-Gabriel, C. J., Schölkopf, B.

Advances in Neural Information Processing Systems 30, pages: 5424-5433, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

arXiv link (url) Project Page [BibTex]

arXiv link (url) Project Page [BibTex]


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The Numerics of GANs

Mescheder, L., Nowozin, S., Geiger, A.

In Proceedings from the conference "Neural Information Processing Systems 2017., (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., Advances in Neural Information Processing Systems 30 (NIPS), December 2017 (inproceedings)

Abstract
In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the associated gradient vector field of GAN training objectives. Our findings suggest that the convergence of current algorithms suffers due to two factors: i) presence of eigenvalues of the Jacobian of the gradient vector field with zero real-part, and ii) eigenvalues with big imaginary part. Using these findings, we design a new algorithm that overcomes some of these limitations and has better convergence properties. Experimentally, we demonstrate its superiority on training common GAN architectures and show convergence on GAN architectures that are known to be notoriously hard to train.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Safe Adaptive Importance Sampling

Stich, S. U., Raj, A., Jaggi, M.

Advances in Neural Information Processing Systems 30, pages: 4384-4394, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets

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

Workshop on Deep Learning for Physical Sciences (DLPS) at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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From Parity to Preference-based Notions of Fairness in Classification

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

Advances in Neural Information Processing Systems 30, pages: 229-239, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Active colloidal propulsion over a crystalline surface

Choudhury, U., Straube, A., Fischer, P., Gibbs, J., Höfling, F.

New Journal of Physics, 19, pages: 125010, December 2017 (article)

Abstract
We study both experimentally and theoretically the dynamics of chemically self-propelled Janus colloids moving atop a two-dimensional crystalline surface. The surface is a hexagonally close-packed monolayer of colloidal particles of the same size as the mobile one. The dynamics of the self-propelled colloid reflects the competition between hindered diffusion due to the periodic surface and enhanced diffusion due to active motion. Which contribution dominates depends on the propulsion strength, which can be systematically tuned by changing the concentration of a chemical fuel. The mean-square displacements obtained from the experiment exhibit enhanced diffusion at long lag times. Our experimental data are consistent with a Langevin model for the effectively two-dimensional translational motion of an active Brownian particle in a periodic potential, combining the confining effects of gravity and the crystalline surface with the free rotational diffusion of the colloid. Approximate analytical predictions are made for the mean-square displacement describing the crossover from free Brownian motion at short times to active diffusion at long times. The results are in semi-quantitative agreement with numerical results of a refined Langevin model that treats translational and rotational degrees of freedom on the same footing.

pf

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Discriminative k-shot learning using probabilistic models

Bauer*, M., Rojas-Carulla*, M., Świątkowski, J. B., Schölkopf, B., Turner, R. E.

Second Workshop on Bayesian Deep Learning at the 31st Conference on Neural Information Processing Systems , December 2017, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Closed-form Inference and Prediction in Gaussian Process State-Space Models

Ialongo, A. D., Van Der Wilk, M., Rasmussen, C. E.

Time Series Workshop at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

PDF [BibTex]

PDF [BibTex]


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Learning Robust Video Synchronization without Annotations

Wieschollek, P., Freeman, I., Lensch, H. P. A.

16th IEEE International Conference on Machine Learning and Applications (ICMLA), pages: 92 - 100, (Editors: X. Chen, B. Luo, F. Luo, V. Palade, and M. A. Wani), IEEE, December 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Optimizing human learning

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

Workshop on Teaching Machines, Robots, and Humans at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

Kim, J., Tabibian, B., Oh, A., Schölkopf, B., Gomez Rodriguez, M.

Workshop on Prioritising Online Content at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Wireless Acoustic-Surface Actuators for Miniaturized Endoscopes

Qiu, T., Adams, F., Palagi, S., Melde, K., Mark, A. G., Wetterauer, U., Miernik, A., Fischer, P.

ACS Applied Materials & Interfaces, 9(49):42536 - 42543, November 2017 (article)

Abstract
Endoscopy enables minimally invasive procedures in many medical fields, such as urology. However, current endoscopes are normally cable-driven, which limits their dexterity and makes them hard to miniaturize. Indeed current urological endoscopes have an outer diameter of about 3 mm and still only possess one bending degree of freedom. In this paper, we report a novel wireless actuation mechanism that increases the dexterity and that permits the miniaturization of a urological endoscope. The novel actuator consists of thin active surfaces that can be readily attached to any device and are wirelessly powered by ultrasound. The surfaces consist of two-dimensional arrays of micro-bubbles, which oscillate under ultrasound excitation and thereby generate an acoustic streaming force. Bubbles of different sizes are addressed by their unique resonance frequency, thus multiple degrees of freedom can readily be incorporated. Two active miniaturized devices (with a side length of around 1 mm) are demonstrated: a miniaturized mechanical arm that realizes two degrees of freedom, and a flexible endoscope prototype equipped with a camera at the tip. With the flexible endoscope, an active endoscopic examination is successfully performed in a rabbit bladder. This results show the potential medical applicability of surface actuators wirelessly powered by ultrasound penetrating through biological tissues.

pf

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals

Tanneberg, D., Peters, J., Rueckert, E.

Proceedings of the 1st Annual Conference on Robot Learning (CoRL), pages: 167-174, Proceedings of Machine Learning Research, (Editors: Sergey Levine, Vincent Vanhoucke and Ken Goldberg), PMLR, November 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows

Huang, B., Zhang, K., Zhang, J., Sanchez-Romero, R., Glymour, C., Schölkopf, B.

IEEE 17th International Conference on Data Mining (ICDM), pages: 913-918, (Editors: Vijay Raghavan,Srinivas Aluru, George Karypis, Lucio Miele and Xindong Wu), November 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Efficient Online Adaptation with Stochastic Recurrent Neural Networks

Tanneberg, D., Peters, J., Rueckert, E.

IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 198-204, IEEE, November 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Learning inverse dynamics models in O(n) time with LSTM networks

Rueckert, E., Nakatenus, M., Tosatto, S., Peters, J.

IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 811-816, IEEE, November 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries

Stark, S., Peters, J., Rueckert, E.

IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages: 624-630, IEEE, November 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Simulation of the underactuated Sake Robotics Gripper in V-REP

Thiem, S., Stark, S., Tanneberg, D., Peters, J., Rueckert, E.

Workshop at the International Conference on Humanoid Robots (HUMANOIDS), November 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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End-to-End Learning for Image Burst Deblurring

Wieschollek, P., Schölkopf, B., Lensch, H. P. A., Hirsch, M.

Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, 10114, pages: 35-51, Image Processing, Computer Vision, Pattern Recognition, and Graphics, (Editors: Lai, S.-H., Lepetit, V., Nishino, K., and Sato, Y. ), Springer, November 2017 (conference)

ei

[BibTex]

[BibTex]


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Active Incremental Learning of Robot Movement Primitives

Maeda, G., Ewerton, M., Osa, T., Busch, B., Peters, J.

Proceedings of the 1st Annual Conference on Robot Learning (CoRL), 78, pages: 37-46, Proceedings of Machine Learning Research, (Editors: Sergey Levine, Vincent Vanhoucke and Ken Goldberg), PMLR, November 2017 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Online Video Deblurring via Dynamic Temporal Blending Network

Kim, T. H., Lee, K. M., Schölkopf, B., Hirsch, M.

Proceedings IEEE International Conference on Computer Vision (ICCV), pages: 4038-4047, IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?

Behl, A., Jafari, O. H., Mustikovela, S. K., Alhaija, H. A., Rother, C., Geiger, A.

In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (inproceedings)

Abstract
Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.

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pdf suppmat Poster Project Page [BibTex]

pdf suppmat Poster Project Page [BibTex]


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EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis

Sajjadi, M. S. M., Schölkopf, B., Hirsch, M.

Proceedings IEEE International Conference on Computer Vision (ICCV), pages: 4501-4510, IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (conference)

ei

Arxiv Project link (url) DOI [BibTex]

Arxiv Project link (url) DOI [BibTex]


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Learning Blind Motion Deblurring

Wieschollek, P., Hirsch, M., Schölkopf, B., Lensch, H.

Proceedings IEEE International Conference on Computer Vision (ICCV), pages: 231-240, IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Sparsity Invariant CNNs

Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., Geiger, A.

International Conference on 3D Vision (3DV) 2017, International Conference on 3D Vision (3DV), October 2017 (conference)

Abstract
In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data. First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the network. To overcome this problem, we propose a simple yet effective sparse convolution layer which explicitly considers the location of missing data during the convolution operation. We demonstrate the benefits of the proposed network architecture in synthetic and real experiments \wrt various baseline approaches. Compared to dense baselines, the proposed sparse convolution network generalizes well to novel datasets and is invariant to the level of sparsity in the data. For our evaluation, we derive a novel dataset from the KITTI benchmark, comprising 93k depth annotated RGB images. Our dataset allows for training and evaluating depth upsampling and depth prediction techniques in challenging real-world settings.

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pdf suppmat Project Page Project Page [BibTex]

pdf suppmat Project Page Project Page [BibTex]


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Personalized Brain-Computer Interface Models for Motor Rehabilitation

Mastakouri, A., Weichwald, S., Ozdenizci, O., Meyer, T., Schölkopf, B., Grosse-Wentrup, M.

Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages: 3024-3029, October 2017 (conference)

ei

ArXiv PDF DOI Project Page [BibTex]

ArXiv PDF DOI Project Page [BibTex]


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OctNetFusion: Learning Depth Fusion from Data

Riegler, G., Ulusoy, A. O., Bischof, H., Geiger, A.

International Conference on 3D Vision (3DV) 2017, International Conference on 3D Vision (3DV), October 2017 (conference)

Abstract
In this paper, we present a learning based approach to depth fusion, i.e., dense 3D reconstruction from multiple depth images. The most common approach to depth fusion is based on averaging truncated signed distance functions, which was originally proposed by Curless and Levoy in 1996. While this method is simple and provides great results, it is not able to reconstruct (partially) occluded surfaces and requires a large number frames to filter out sensor noise and outliers. Motivated by the availability of large 3D model repositories and recent advances in deep learning, we present a novel 3D CNN architecture that learns to predict an implicit surface representation from the input depth maps. Our learning based method significantly outperforms the traditional volumetric fusion approach in terms of noise reduction and outlier suppression. By learning the structure of real world 3D objects and scenes, our approach is further able to reconstruct occluded regions and to fill in gaps in the reconstruction. We demonstrate that our learning based approach outperforms both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric fusion. Further, we demonstrate state-of-the-art 3D shape completion results.

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pdf Video 1 Video 2 Project Page Project Page [BibTex]

pdf Video 1 Video 2 Project Page Project Page [BibTex]


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Generalized exploration in policy search

van Hoof, H., Tanneberg, D., Peters, J.

Machine Learning, 106(9-10):1705-1724 , (Editors: Kurt Driessens, Dragi Kocev, Marko Robnik‐Sikonja, and Myra Spiliopoulou), October 2017, Special Issue of the ECML PKDD 2017 Journal Track (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Multi-frame blind image deconvolution through split frequency - phase recovery

Gauci, A., Abela, J., Cachia, E., Hirsch, M., ZarbAdami, K.

Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), pages: 1022511, (Editors: Yulin Wang, Tuan D. Pham, Vit Vozenilek, David Zhang, Yi Xie), October 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Probabilistic Prioritization of Movement Primitives

Paraschos, A., Lioutikov, R., Peters, J., Neumann, G.

Proceedings of the International Conference on Intelligent Robot Systems, and IEEE Robotics and Automation Letters (RA-L), 2(4):2294-2301, October 2017 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Active Acoustic Surfaces Enable the Propulsion of a Wireless Robot

Qiu, T., Palagi, S., Mark, A. G., Melde, K., Adams, F., Fischer, P.

Advanced Materials Interfaces, 4(21):1700933, September 2017 (article)

Abstract
A major challenge that prevents the miniaturization of mechanically actuated systems is the lack of suitable methods that permit the efficient transfer of power to small scales. Acoustic energy holds great potential, as it is wireless, penetrates deep into biological tissues, and the mechanical vibrations can be directly converted into directional forces. Recently, active acoustic surfaces are developed that consist of 2D arrays of microcavities holding microbubbles that can be excited with an external acoustic field. At resonance, the surfaces give rise to acoustic streaming and thus provide a highly directional propulsive force. Here, this study advances these wireless surface actuators by studying their force output as the size of the bubble-array is increased. In particular, a general method is reported to dramatically improve the propulsive force, demonstrating that the surface actuators are actually able to propel centimeter-scale devices. To prove the flexibility of the functional surfaces as wireless ready-to-attach actuator, a mobile mini-robot capable of propulsion in water along multiple directions is presented. This work paves the way toward effectively exploiting acoustic surfaces as a novel wireless actuation scheme at small scales.

pf

link (url) DOI Project Page [BibTex]


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Direct Visual Odometry for a Fisheye-Stereo Camera

Liu, P., Heng, L., Sattler, T., Geiger, A., Pollefeys, M.

In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2017 (inproceedings)

Abstract
We present a direct visual odometry algorithm for a fisheye-stereo camera. Our algorithm performs simultaneous camera motion estimation and semi-dense reconstruction. The pipeline consists of two threads: a tracking thread and a mapping thread. In the tracking thread, we estimate the camera pose via semi-dense direct image alignment. To have a wider field of view (FoV) which is important for robotic perception, we use fisheye images directly without converting them to conventional pinhole images which come with a limited FoV. To address the epipolar curve problem, plane-sweeping stereo is used for stereo matching and depth initialization. Multiple depth hypotheses are tracked for selected pixels to better capture the uncertainty characteristics of stereo matching. Temporal motion stereo is then used to refine the depth and remove false positive depth hypotheses. Our implementation runs at an average of 20 Hz on a low-end PC. We run experiments in outdoor environments to validate our algorithm, and discuss the experimental results. We experimentally show that we are able to estimate 6D poses with low drift, and at the same time, do semi-dense 3D reconstruction with high accuracy.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Closing One’s Eyes Affects Amplitude Modulation but Not Frequency Modulation in a Cognitive BCI

Görner, M., Schölkopf, B., Grosse-Wentrup, M.

Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 165-170, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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A Guided Task for Cognitive Brain-Computer Interfaces

Moser, J., Hohmann, M. R., Schölkopf, B., Grosse-Wentrup, M.

Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 326-331, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Bayesian Regression for Artifact Correction in Electroencephalography

Fiebig, K., Jayaram, V., Hesse, T., Blank, A., Peters, J., Grosse-Wentrup, M.

Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 131-136, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)

am ei

DOI [BibTex]

DOI [BibTex]


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Investigating Music Imagery as a Cognitive Paradigm for Low-Cost Brain-Computer Interfaces

Grossberger, L., Hohmann, M. R., Peters, J., Grosse-Wentrup, M.

Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 160-164, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)

am ei

DOI [BibTex]

DOI [BibTex]


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Correlations of Motor Adaptation Learning and Modulation of Resting-State Sensorimotor EEG Activity

Ozdenizci, O., Yalcin, M., Erdogan, A., Patoglu, V., Grosse-Wentrup, M., Cetin, M.

Proceedings of the 7th Graz Brain-Computer Interface Conference 2017 - From Vision to Reality, pages: 384-388, (Editors: Müller-Putz G.R., Steyrl D., Wriessnegger S. C., Scherer R.), Graz University of Technology, Austria, Graz Brain-Computer Interface Conference, September 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Corrosion-Protected Hybrid Nanoparticles

Jeong, H. H., Alarcon-Correa, M., Mark, A. G., Son, K., Lee, T., Fischer, P.

Advanced Science, 4(12):1700234, September 2017 (article)

Abstract
Nanoparticles composed of functional materials hold great promise for applications due to their unique electronic, optical, magnetic, and catalytic properties. However, a number of functional materials are not only difficult to fabricate at the nanoscale, but are also chemically unstable in solution. Hence, protecting nanoparticles from corrosion is a major challenge for those applications that require stability in aqueous solutions and biological fluids. Here, this study presents a generic scheme to grow hybrid 3D nanoparticles that are completely encapsulated by a nm thick protective shell. The method consists of vacuum-based growth and protection, and combines oblique physical vapor deposition with atomic layer deposition. It provides wide flexibility in the shape and composition of the nanoparticles, and the environments against which particles are protected. The work demonstrates the approach with multifunctional nanoparticles possessing ferromagnetic, plasmonic, and chiral properties. The present scheme allows nanocolloids, which immediately corrode without protection, to remain functional, at least for a week, in acidic solutions.

pf

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Weakly-Supervised Localization of Diabetic Retinopathy Lesions in Retinal Fundus Images

Gondal, M. W., Köhler, J. M., Grzeszick, R., Fink, G., Hirsch, M.

IEEE International Conference on Image Processing (ICIP), pages: 2069-2073, September 2017 (conference)

ei

arXiv DOI [BibTex]

arXiv DOI [BibTex]


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Assisting the practice of motor skills by humans with a probability distribution over trajectories

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

In Workshop Human-in-the-loop robotic manipulation: on the influence of the human role at IROS, September 2017 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


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BIMROB – Bidirectional Interaction Between Human and Robot for the Learning of Movements

Kollegger, G., Ewerton, M., Wiemeyer, J., Peters, J.

Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS), (663):151-163, Advances in Intelligent Systems and Computing, (Editors: Lames M., Saupe D. and Wiemeyer J.), Springer International Publishing, September 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Goal-driven dimensionality reduction for reinforcement learning

Parisi, S., Ramstedt, S., Peters, J.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 4634-4639, IEEE, September 2017 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Augmented Reality Meets Deep Learning for Car Instance Segmentation in Urban Scenes

Alhaija, H. A., Mustikovela, S. K., Mescheder, L., Geiger, A., Rother, C.

In Proceedings of the British Machine Vision Conference 2017, Proceedings of the British Machine Vision Conference, September 2017 (inproceedings)

Abstract
The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D shapes of the target object category. We demonstrate the utility of the proposed approach for training a state-of-the-art high-capacity deep model for semantic instance segmentation. In particular, we consider the task of segmenting car instances on the KITTI dataset which we have annotated with pixel-accurate ground truth. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amounts of annotated real data.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Hybrid control trajectory optimization under uncertainty

Pajarinen, J., Kyrki, V., Koval, M., Srinivasa, S., Peters, J., Neumann, G.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 5694-5701, September 2017 (conference)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Local Bayesian Optimization of Motor Skills

Akrour, R., Sorokin, D., Peters, J., Neumann, G.

Proceedings of the 34th International Conference on Machine Learning, 70, pages: 41-50, Proceedings of Machine Learning Research, (Editors: Doina Precup, Yee Whye Teh), PMLR, International Conference on Machine Learning (ICML), August 2017 (conference)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]