Maximilian Soelch
Maximilian Soelch
Research Scientist, Data:Lab, Volkswagen AG | PhD Student, Technical University of Munich
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
1882016
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
622016
Unsupervised real-time control through variational empowerment
M Karl, M Soelch, P Becker-Ehmck, D Benbouzid, P van der Smagt, ...
arXiv preprint arXiv:1710.05101, 2017
332017
Approximate bayesian inference in spatial environments
A Mirchev, B Kayalibay, M Soelch, P van der Smagt, J Bayer
arXiv preprint arXiv:1805.07206, 2018
112018
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
42019
On deep set learning and the choice of aggregations
M Soelch, A Akhundov, P van der Smagt, J Bayer
International Conference on Artificial Neural Networks, 444-457, 2019
42019
Detecting anomalies in robot time series data using stochastic recurrent networks
M Sölch
42015
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
12018
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
2021
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