| 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 | 357 | 2016 |
| Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series M Soelch, J Bayer, M Ludersdorfer, P van der Smagt arXiv preprint arXiv:1602.07109, 2016 | 94 | 2016 |
| Unsupervised real-time control through variational empowerment M Karl, P Becker-Ehmck, M Soelch, D Benbouzid, P van der Smagt, ... Robotics Research: The 19th International Symposium ISRR, 158-173, 2022 | 51 | 2022 |
| Latent matters: Learning deep state-space models A Klushyn, R Kurle, M Soelch, B Cseke, P van der Smagt Advances in Neural Information Processing Systems 34, 10234-10245, 2021 | 21 | 2021 |
| Approximate bayesian inference in spatial environments A Mirchev, B Kayalibay, M Soelch, P van der Smagt, J Bayer arXiv preprint arXiv:1805.07206, 2018 | 19 | 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 | 10 | 2019 |
| Mind the gap when conditioning amortised inference in sequential latent-variable models J Bayer, M Soelch, A Mirchev, B Kayalibay, P van der Smagt arXiv preprint arXiv:2101.07046, 2021 | 8 | 2021 |
| Variational tracking and prediction with generative disentangled state-space models A Akhundov, M Soelch, J Bayer, P van der Smagt arXiv preprint arXiv:1910.06205, 2019 | 5 | 2019 |
| Detecting anomalies in robot time series data using stochastic recurrent networks M Sölch | 5 | 2015 |
| Navigation and planning in latent maps B Kayalibay, A Mirchev, M Soelch, P Van Der Smagt, J Bayer FAIM workshop “Prediction and Generative Modeling in Reinforcement Learning 4, 2018 | 2 | 2018 |
| Uncovering dynamics MJG Sölch Technische Universität München, 2021 | | 2021 |
