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Maximilian Soelch
Maximilian Soelch
Machine Learning Research Lab, Volkswagen AG
Verified email at argmax.ai - Homepage
Title
Cited by
Cited by
Year
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
3422016
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
922016
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
482022
Approximate bayesian inference in spatial environments
A Mirchev, B Kayalibay, M Soelch, P van der Smagt, J Bayer
arXiv preprint arXiv:1805.07206, 2018
192018
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
132021
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
102019
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
82021
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
52019
Detecting anomalies in robot time series data using stochastic recurrent networks
M Sölch
52015
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
22018
Uncovering dynamics
MJG Sölch
Technische Universität München, 2021
2021
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