Maximilian Soelch
Maximilian Soelch
Machine Learning Research Lab, Volkswagen AG
Geverifieerd e-mailadres voor argmax.ai - Homepage
Geciteerd door
Geciteerd door
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
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
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
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
Approximate bayesian inference in spatial environments
A Mirchev, B Kayalibay, M Soelch, P van der Smagt, J Bayer
arXiv preprint arXiv:1805.07206, 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
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
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
Detecting anomalies in robot time series data using stochastic recurrent networks
M Sölch
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
Uncovering dynamics
MJG Sölch
Technische Universität München, 2021
Het systeem kan de bewerking nu niet uitvoeren. Probeer het later opnieuw.
Artikelen 1–11