| Parameter-exploring policy gradients F Sehnke, C Osendorfer, T Rückstieß, A Graves, J Peters, J Schmidhuber Neural Networks 23 (4), 551-559, 2010 | 230 | 2010 |
| Learning stochastic recurrent networks J Bayer, C Osendorfer arXiv preprint arXiv:1411.7610, 2014 | 194 | 2014 |
| Policy gradients with parameter-based exploration for control F Sehnke, C Osendorfer, T Rückstieß, A Graves, J Peters, J Schmidhuber International Conference on Artificial Neural Networks, 387-396, 2008 | 74 | 2008 |
| Image super-resolution with fast approximate convolutional sparse coding C Osendorfer, H Soyer, P Van Der Smagt International Conference on Neural Information Processing, 250-257, 2014 | 72 | 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 | 68 | 2013 |
| Sequential feature selection for classification T Rückstieß, C Osendorfer, P van der Smagt Australasian Joint Conference on Artificial Intelligence, 132-141, 2011 | 54 | 2011 |
| 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 | 46 | 2011 |
| 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 | 42 | 2015 |
| 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, 3025-3035, 2018 | 33 | 2018 |
| Convolutional neural networks learn compact local image descriptors C Osendorfer, J Bayer, S Urban, P Van Der Smagt International Conference on Neural Information Processing, 624-630, 2013 | 25 | 2013 |
| 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 | 20 | 2014 |
| Minimizing data consumption with sequential online feature selection T Rückstieß, C Osendorfer, P van der Smagt International Journal of Machine Learning and Cybernetics 4 (3), 235-243, 2013 | 20 | 2013 |
| Multimodal parameter-exploring policy gradients F Sehnke, A Graves, C Osendorfer, J Schmidhuber 2010 Ninth International Conference on Machine Learning and Applications …, 2010 | 19 | 2010 |
| 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 | 17 | 2014 |
| 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 | 14 | 2013 |
| Policy gradients for cryptanalysis F Sehnke, C Osendorfer, J Sölter, J Schmidhuber, U Rührmair International Conference on Artificial Neural Networks, 168-177, 2010 | 10 | 2010 |
| Training neural networks with implicit variance J Bayer, C Osendorfer, S Urban, P van der Smagt International Conference on Neural Information Processing, 132-139, 2013 | 9 | 2013 |
| Learning sequence neighbourhood metrics J Bayer, C Osendorfer, P Van Der Smagt International Conference on Artificial Neural Networks, 531-538, 2012 | 9 | 2012 |
| Unsupervised feature learning for low-level local image descriptors C Osendorfer, J Bayer, S Urban, P Van Der Smagt arXiv preprint arXiv:1301.2840, 2013 | 7 | 2013 |
| Unsupervised learning of low-level audio features for music similarity estimation C Osendorfer, J Schlüter, J Schmidhuber, P van der Smagt Proceedings, 2011 | 7 | 2011 |
Patrick van der SmagtVerified email at tum.de
