Justin Bayer
Justin Bayer
Volkswagen AG
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Zitiert von
Zitiert von
Theano: A Python framework for fast computation of mathematical expressions
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
arXiv e-prints, arXiv: 1605.02688, 2016
T Schaul, J Bayer, D Wierstra, Y Sun, M Felder, F Sehnke, T Rückstieß, ...
Journal of Machine Learning Research 11 (ARTICLE), 743-746, 2010
Deep variational bayes filters: Unsupervised learning of state space models from raw data
M Karl, M Soelch, J Bayer, P Van der Smagt
arXiv preprint arXiv:1605.06432, 2016
Learning stochastic recurrent networks
J Bayer, C Osendorfer
arXiv preprint arXiv:1411.7610, 2014
Evolving memory cell structures for sequence learning
J Bayer, D Wierstra, J Togelius, J Schmidhuber
Artificial Neural Networks–ICANN 2009: 19th International Conference …, 2009
Metrics for deep generative models
N Chen, A Klushyn, R Kurle, X Jiang, J Bayer, P Smagt
International Conference on Artificial Intelligence and Statistics, 1540-1550, 2018
Variational inference for on-line anomaly detection in high-dimensional time series
M Sölch, J Bayer, M Ludersdorfer, P van der Smagt
arXiv preprint arXiv:1602.07109, 2016
On Fast Dropout and its Applicability to Recurrent Networks
J Bayer, C Osendorfer, N Chen, P van der Smagt
preprint arXiv:1311.0701, 2013
Unsupervised real-time control through variational empowerment
M Karl, P Becker-Ehmck, M Soelch, D Benbouzid, P van der Smagt, ...
The International Symposium of Robotics Research, 158-173, 2019
Efficient movement representation by embedding dynamic movement primitives in deep autoencoders
N Chen, J Bayer, S Urban, P Van Der Smagt
2015 IEEE-RAS 15th international conference on humanoid robots (Humanoids …, 2015
Learning sequence representations
JS Bayer
Technische Universität München, 2015
Continuous robot control using surface electromyography of atrophic muscles
J Vogel, J Bayer, P Van Der Smagt
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2013
Learning flat latent manifolds with vaes
N Chen, A Klushyn, F Ferroni, J Bayer, P Van Der Smagt
arXiv preprint arXiv:2002.04881, 2020
Bayesian Learning of Neural Network Architectures
G Dikov, P van der Smagt, J Bayer
arXiv preprint arXiv:1901.04436, 2019
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
Key insights into hand biomechanics: human grip stiffness can be decoupled from force by cocontraction and predicted from electromyography
H Höppner, M Große-Dunker, G Stillfried, J Bayer, P Van Der Smagt
Frontiers in neurorobotics 11, 17, 2017
Fast approximate geodesics for deep generative models
N Chen, F Ferroni, A Klushyn, A Paraschos, J Bayer, P van der Smagt
Artificial Neural Networks and Machine Learning–ICANN 2019: Deep Learning …, 2019
Approximate bayesian inference in spatial environments
A Mirchev, B Kayalibay, M Soelch, P van der Smagt, J Bayer
arXiv preprint arXiv:1805.07206, 2018
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
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
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