| 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 | 1212* | 2016 |
| 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 | 471 | 2016 |
| PyBrain T Schaul, J Bayer, D Wierstra, Y Sun, M Felder, F Sehnke, T Rückstieß, ... Massachusetts Institute of Technology Press 11, 743-746, 2010 | 467 | 2010 |
| Learning stochastic recurrent networks J Bayer, C Osendorfer arXiv preprint arXiv:1411.7610, 2014 | 359 | 2014 |
| Evolving memory cell structures for sequence learning J Bayer, D Wierstra, J Togelius, J Schmidhuber International conference on artificial neural networks, 755-764, 2009 | 153 | 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 | 141 | 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 | 116 | 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 | 94 | 2013 |
| 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 | 64 | 2015 |
| 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 | 62 | 2019 |
| Learning sequence representations JS Bayer Technische Universität München, 2015 | 54 | 2015 |
| Learning flat latent manifolds with vaes N Chen, A Klushyn, F Ferroni, J Bayer, P Van Der Smagt arXiv preprint arXiv:2002.04881, 2020 | 51 | 2020 |
| 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 | 42 | 2013 |
| Bayesian Learning of Neural Network Architectures G Dikov, P van der Smagt, J Bayer arXiv preprint arXiv:1901.04436, 2019 | 40 | 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 | 31 | 2013 |
| 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 | 30 | 2019 |
| 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 | 27 | 2017 |
| Approximate bayesian inference in spatial environments A Mirchev, B Kayalibay, M Soelch, P van der Smagt, J Bayer arXiv preprint arXiv:1805.07206, 2018 | 26 | 2018 |
| On deep set learning and the choice of aggregations M Soelch, A Akhundov, P van der Smagt, J Bayer Artificial Neural Networks and Machine Learning–ICANN 2019: Theoretical …, 2019 | 25 | 2019 |
| 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 | 22 | 2013 |
Patrick van der SmagtELTE UniversityVerifisert e-postadresse på tum.de
