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Christian Osendorfer (Dr.)
Christian Osendorfer (Dr.)
former TU München
Email confirmado em in.tum.de - Página inicial
Título
Citado por
Citado por
Ano
Learning stochastic recurrent networks
J Bayer, C Osendorfer
arXiv preprint arXiv:1411.7610, 2014
3402014
Parameter-exploring policy gradients
F Sehnke, C Osendorfer, T Rückstieß, A Graves, J Peters, J Schmidhuber
Neural Networks 23 (4), 551-559, 2010
3402010
Sequential feature selection for classification
T Rückstieß, C Osendorfer, P Van Der Smagt
AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint …, 2011
1032011
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
1022008
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
942014
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
882013
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
762020
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
722015
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
602018
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
542011
Differentiable Iterative Surface Normal Estimation
JE Lenssen, C Osendorfer, J Masci
https://arxiv.org/abs/1904.07172, 2019
49*2019
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
432014
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
312013
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
232013
Multimodal parameter-exploring policy gradients
F Sehnke, A Graves, C Osendorfer, J Schmidhuber
2010 Ninth International Conference on Machine Learning and Applications …, 2010
212010
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
202013
Recurrent neural processes
T Willi, J Masci, J Schmidhuber, C Osendorfer
arXiv preprint arXiv:1906.05915, 2019
192019
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
192014
Policy gradients for cryptanalysis
F Sehnke, C Osendorfer, J Sölter, J Schmidhuber, U Rührmair
Artificial Neural Networks–ICANN 2010: 20th International Conference …, 2010
152010
Learning sequence neighbourhood metrics
J Bayer, C Osendorfer, P van der Smagt
International Conference on Artificial Neural Networks, 531-538, 2012
132012
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Artigos 1–20