2368 results (BibTeX)

2017


Automatic detection of motion artifacts in MR images using CNNS

Meding, K., Loktyushin, A., Hirsch, M.

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), pages: 811-815, 2017 (conference)

ei

DOI [BibTex]

2017


DOI [BibTex]


Ecological feedback in quorum-sensing microbial populations can induce heterogeneous production of autoinducers

Bauer*, M., Knebel*, J., Lechner, M., Pickl, P., Frey, E.

{eLife}, July 2017, *equal contribution (article)

ei

DOI [BibTex]

DOI [BibTex]


Minimax Estimation of Kernel Mean Embeddings

Tolstikhin, I., Sriperumbudur, B., Muandet, K.

Journal of Machine Learning Research, 18, pages: 1-47, 2017 (article) To be published

ei

[BibTex]

[BibTex]


Lost Relatives of the Gumbel Trick

Balog, M., Tripuraneni, N., Ghahramani, Z., Weller, A.

Proceedings of the 34th International Conference on Machine Learning (ICML 2017), 70, pages: 371-379, Proceedings of Machine Learning Research, (Editors: Doina Precup and Yee Whye Teh), PMLR, 2017 (conference)

ei

Code link (url) [BibTex]

Code link (url) [BibTex]


Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates

Gu*, S., Holly*, E., Lillicrap, T., Levine, S.

IEEE International Conference on Robotics and Automation (ICRA 2017), 2017, *equal contribution (conference)

ei

Arxiv [BibTex]

Arxiv [BibTex]


Categorical Reparametrization with Gumble-Softmax

Jang, E., Gu, S., Poole, B.

5th International Conference on Learning Representations (ICLR 2017), 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

Gu, S., Lillicrap, T., Ghahramani, Z., Turner, R., Levine, S.

5th International Conference on Learning Representations (ICLR 2017), 2017 (conference)

ei

PDF [BibTex]

PDF [BibTex]


Sequence Tutor: Conservative fine-tuning of sequence generation models with KL-control

Jaques, N., Gu, S., Bahdanau, D., Hernández-Lobato, J., Turner, R., Eck, D.

Proceedings of the 34th International Conference on Machine Learning (ICML 2017), 70, pages: 1645-1654, Proceedings of Machine Learning Research, (Editors: Doina Precup and Yee Whye Te), PMLR, 2017 (conference)

ei

Arxiv link (url) [BibTex]

Arxiv link (url) [BibTex]


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

Huang, B., Zhang, K., Zhang, J., Glymour, C., Schölkopf, B.

IEEE 17th International Conference on Data Mining (ICDM 2017), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


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 2017), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


Learning Blind Motion Deblurring

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

IEEE International Conference on Computer Vision (ICCV 2017), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


Online Video Deblurring via Dynamic Temporal Blending Network

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

IEEE International Conference on Computer Vision (ICCV 2017), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


Causal Consistency of Structural Equation Models

Rubenstein*, P., Weichwald*, S., Bongers, S., Mooij, J., Janzing, D., Grosse-Wentrup, M., Schölkopf, B.

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence (UAI 2017), 2017, *equal contribution (conference) Accepted

ei

Arxiv [BibTex]

Arxiv [BibTex]


Comparing sensitivity estimates from MLDS and forced-choice methods in a slant-from-texture experiment

Aguilar, G., Wichmann, F., Maertens, M.

Journal of Vision, 17(1), 2017 (article)

ei

DOI [BibTex]


Detecting distortions of peripherally presented letter stimuli under crowded conditions

Wallis, T., Tobias, S., Bethge, M., Wichmann, F.

Attention, Perception, & Psychophysics, 79(3):850-862, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


Causal Discovery from Temporally Aggregated Time Series

Gong, M., Zhang, K., Schölkopf, B., Glymour, C., Tao, D.

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence (UAI 2017), 2017, ID 269 (conference) Accepted

ei

[BibTex]

[BibTex]


Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination

Zhang, K., Huang, B., Zhang, J., Glymour, C., Schölkopf, B.

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017), 2017 (conference) Accepted

ei

PDF [BibTex]

PDF [BibTex]


Elements of Causal Inference - Foundations and Learning Algorithms

Peters, J., Janzing, D., Schölkopf, B.

Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book) In press

ei

PDF [BibTex]

PDF [BibTex]


Approximate Steepest Coordinate Descent

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

Proceedings of the 34th International Conference on Machine Learning (ICML 2017), 70, pages: 3251-3259, Proceedings of Machine Learning Research, (Editors: Doina Precup and Yee Whye Teh), PMLR, 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Local Group Invariant Representations via Orbit Embeddings

Raj, A., Kumar, A., Mroueh, Y., Fletcher, T., Schölkopf, B.

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017), 54, pages: 1225-1235, Proceedings of Machine Learning Research, (Editors: Aarti Singh and Jerry Zhu), 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Local Bayesian Optimization of Motor Skills

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

Proceedings of the 34th International Conference on Machine Learning (ICML 2017), 2017 (conference) Accepted

am ei

[BibTex]

[BibTex]


Pre-Movement Contralateral EEG Low Beta Power Is Modulated with Motor Adaptation Learning

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

Proceedings of the 7th Graz Brain-Computer Interface Conference (GBCIC 2017), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


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 (GBCIC 2017), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


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

Grossberger, L., Hohmann, M., Peters, J., M., G.

Proceedings of the 7th Graz Brain-Computer Interface Conference (GBCIC 2017), 2017 (conference) Accepted

am ei

[BibTex]

[BibTex]


Bayesian Regression for Artifact Correction in Electroencephalography

Fiebig, K., Jayaram, V., Hesse, T., Blank, A., Peters, J., M., G.

Proceedings of the 7th Graz Brain-Computer Interface Conference (GBCIC 2017), 2017 (conference) Accepted

am ei

[BibTex]

[BibTex]


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 (GBCIC 2017) , 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


A Guided Task for Cognitive Brain-Computer Interfaces

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

Proceedings of the 7th Graz Brain-Computer Interface Conference (GBCIC 2017), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


Electroencephalographic identifiers of motor adaptation learning

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

Journal of Neural Engineering, 2017 (article) Submitted

ei

[BibTex]

[BibTex]


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

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

IEEE International Conference on Image Processing (ICIP 207), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


Dynamic Time-of-Flight

Schober, M., Adam, A., Yair, O., Mazor, S., Nowozin, S.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017 (conference) Accepted

ei pn

[BibTex]

[BibTex]


Discovering Causal Signals in Images

Lopez-Paz, D., Nishihara, R., Chintala, S., Schölkopf, B., Bottou, L.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Flexible Spatio-Temporal Networks for Video Prediction

Lu, C., Hirsch, M., Schölkopf, B.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Frequency Peak Features for Low-Channel Classification in Motor Imagery Paradigms

Jayaram, V., Schölkopf, B., Grosse-Wentrup, M.

Proceedings of the 8th International IEEE EMBS Conference on Neural Engineering (NER 2017), 2017 (conference) Accepted

ei

[BibTex]

[BibTex]


AdaGAN: Boosting Generative Models

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

2017 (conference) Submitted

ei

Arxiv [BibTex]

Arxiv [BibTex]


DeepCoder: Learning to Write Programs

Balog, M., Gaunt, A., Brockschmidt, M., Nowozin, S., Tarlow, D.

5th International Conference on Learning Representations (ICLR 2017), 2017 (conference) Accepted

ei

Arxiv [BibTex]

Arxiv [BibTex]


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), 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


Thumb md 2016 enhancenet
EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis

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

arXiv:1612.07919, IEEE International Conference on Computer Vision (ICCV 2017), 2017 (talk) Accepted

ei

Arxiv [BibTex]

Arxiv [BibTex]


Thumb md reliability icon
Distilling Information Reliability and Source Trustworthiness from Digital Traces

Tabibian, B., Valera, I., Farajtabar, M., Song, L., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 26th International Conference on World Wide Web (WWW 2017), pages: 847-855, (Editors: Barrett, R., Cummings, R., Agichtein, E. and Gabrilovich, E. ), ACM, 2017 (conference)

ei

Project DOI [BibTex]

Project DOI [BibTex]


DiSMEC – Distributed Sparse Machines for Extreme Multi-label Classification

Babbar, R., Schölkopf, B.

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM 2017), pages: 721-729, 2017 (conference)

ei

DOI [BibTex]

DOI [BibTex]


BundleMAP: Anatomically Localized Classification, Regression, and Hypothesis Testing in Diffusion MRI

Khatami, M., Schmidt-Wilcke, T., Sundgren, P., Abbasloo, A., Schölkopf, B., Schultz, T.

Pattern Recognition, 63, pages: 593-600, 2017 (article)

ei

DOI [BibTex]

DOI [BibTex]


End-to-End Learning for Image Burst Deblurring

Wieschollek, P., Schölkopf, B., Lensch, H., 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, 2017 (conference)

ei

[BibTex]

[BibTex]


Unsupervised clustering of EOG as a viable substitute for optical eye-tracking

Flad, N., Fomina, T., Bülthoff, H., Chuang, L.

In First Workshop on Eye Tracking and Visualization (ETVIS 2015), pages: 151-167, Mathematics and Visualization, (Editors: Burch, M., Chuang, L., Fisher, B., Schmidt, A., and Weiskopf, D.), Springer, 2017 (inbook)

ei

DOI [BibTex]

DOI [BibTex]


Model Selection for Gaussian Mixture Models

Huang, T., Peng, H., Zhang, K.

Statistica Sinica, 27(1):147-169, 2017 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills

Kupcsik, A., Deisenroth, M., Peters, J., Ai Poh, L., Vadakkepat, V., Neumann, G.

Artificial Intelligence, 247, pages: 415-439, 2017, Special Issue on AI and Robotics (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Anticipatory Action Selection for Human-Robot Table Tennis

Wang, Z., Boularias, A., Mülling, K., Schölkopf, B., Peters, J.

Artificial Intelligence, 247, pages: 399-414, 2017, Special Issue on AI and Robotics (article)

Abstract
Abstract Anticipation can enhance the capability of a robot in its interaction with humans, where the robot predicts the humans' intention for selecting its own action. We present a novel framework of anticipatory action selection for human-robot interaction, which is capable to handle nonlinear and stochastic human behaviors such as table tennis strokes and allows the robot to choose the optimal action based on prediction of the human partner's intention with uncertainty. The presented framework is generic and can be used in many human-robot interaction scenarios, for example, in navigation and human-robot co-manipulation. In this article, we conduct a case study on human-robot table tennis. Due to the limited amount of time for executing hitting movements, a robot usually needs to initiate its hitting movement before the opponent hits the ball, which requires the robot to be anticipatory based on visual observation of the opponent's movement. Previous work on Intention-Driven Dynamics Models (IDDM) allowed the robot to predict the intended target of the opponent. In this article, we address the problem of action selection and optimal timing for initiating a chosen action by formulating the anticipatory action selection as a Partially Observable Markov Decision Process (POMDP), where the transition and observation are modeled by the \{IDDM\} framework. We present two approaches to anticipatory action selection based on the \{POMDP\} formulation, i.e., a model-free policy learning method based on Least-Squares Policy Iteration (LSPI) that employs the \{IDDM\} for belief updates, and a model-based Monte-Carlo Planning (MCP) method, which benefits from the transition and observation model by the IDDM. Experimental results using real data in a simulated environment show the importance of anticipatory action selection, and that \{POMDPs\} are suitable to formulate the anticipatory action selection problem by taking into account the uncertainties in prediction. We also show that existing algorithms for POMDPs, such as \{LSPI\} and MCP, can be applied to substantially improve the robot's performance in its interaction with humans.

am ei

DOI [BibTex]

DOI [BibTex]