| Parameter-exploring policy gradients F Sehnke, C Osendorfer, T Rückstieß, A Graves, J Peters, J Schmidhuber Neural Networks 23 (4), 551-559, 2010 | 368 | 2010 |
| Learning stochastic recurrent networks J Bayer, C Osendorfer arXiv preprint arXiv:1411.7610, 2014 | 364 | 2014 |
| Sequential feature selection for classification T Rückstieß, C Osendorfer, P Van Der Smagt AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint …, 2011 | 122 | 2011 |
| Policy gradients with parameter-based exploration for control F Sehnke, C Osendorfer, T Rückstieß, A Graves, J Peters, J Schmidhuber Artificial Neural Networks-ICANN 2008: 18th International Conference, Prague …, 2008 | 109 | 2008 |
| Image super-resolution with fast approximate convolutional sparse coding C Osendorfer, H Soyer, P Van Der Smagt Neural Information Processing: 21st International Conference, ICONIP 2014 …, 2014 | 98 | 2014 |
| On fast dropout and its applicability to recurrent networks J Bayer, C Osendorfer, D Korhammer, N Chen, S Urban, P van der Smagt arXiv preprint arXiv:1311.0701, 2013 | 94 | 2013 |
| Two-stage peer-regularized feature recombination for arbitrary image style transfer J Svoboda, A Anoosheh, C Osendorfer, J Masci Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 88 | 2020 |
| Using tactile sensation for learning contact knowledge: Discriminate collision from physical interaction S Golz, C Osendorfer, S Haddadin 2015 IEEE International Conference on Robotics and Automation (ICRA), 3788-3794, 2015 | 79 | 2015 |
| Differentiable Iterative Surface Normal Estimation JE Lenssen, C Osendorfer, J Masci https://arxiv.org/abs/1904.07172, 2019 | 68* | 2019 |
| Nais-net: Stable deep networks from non-autonomous differential equations M Ciccone, M Gallieri, J Masci, C Osendorfer, F Gomez Advances in Neural Information Processing Systems 31, 2018 | 65 | 2018 |
| Music similarity estimation with the mean-covariance restricted Boltzmann machine J Schluter, C Osendorfer 2011 10th International conference on machine learning and applications and …, 2011 | 55 | 2011 |
| Model-free robot anomaly detection R Hornung, H Urbanek, J Klodmann, C Osendorfer, P Van Der Smagt 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2014 | 52 | 2014 |
| Convolutional neural networks learn compact local image descriptors C Osendorfer, J Bayer, S Urban, P van der Smagt Neural Information Processing: 20th International Conference, ICONIP 2013 …, 2013 | 31 | 2013 |
| Minimizing data consumption with sequential online feature selection T Rückstieß, C Osendorfer, P van der Smagt International Journal of Machine Learning and Cybernetics 4, 235-243, 2013 | 24 | 2013 |
| Recurrent neural processes T Willi, J Masci, J Schmidhuber, C Osendorfer arXiv preprint arXiv:1906.05915, 2019 | 22 | 2019 |
| Multimodal parameter-exploring policy gradients F Sehnke, A Graves, C Osendorfer, J Schmidhuber 2010 Ninth International Conference on Machine Learning and Applications …, 2010 | 21 | 2010 |
| Computing grip force and torque from finger nail images using gaussian processes S Urban, J Bayer, C Osendorfer, G Westling, BB Edin, P Van Der Smagt 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2013 | 20 | 2013 |
| Estimating finger grip force from an image of the hand using convolutional neural networks and gaussian processes N Chen, S Urban, C Osendorfer, J Bayer, P Van Der Smagt 2014 IEEE International Conference on Robotics and Automation (ICRA), 3137-3142, 2014 | 19 | 2014 |
| Policy gradients for cryptanalysis F Sehnke, C Osendorfer, J Sölter, J Schmidhuber, U Rührmair Artificial Neural Networks–ICANN 2010: 20th International Conference …, 2010 | 15 | 2010 |
| Learning sequence neighbourhood metrics J Bayer, C Osendorfer, P van der Smagt International Conference on Artificial Neural Networks, 531-538, 2012 | 13 | 2012 |
Patrick van der SmagtVolkswagen AG; ELTE UniversityZweryfikowany adres z tum.de
