Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series. - Maximilian Soelch 001; Justin Bayer; Marvin Ludersdorfer; Patrick van der Smagt (CoRR, 2016)

Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data. - Maximilian Karl; Maximilian Soelch 001; Justin Bayer; Patrick van der Smagt (ICLR (Poster), 2017)

On Deep Set Learning and the Choice of Aggregations. - Maximilian Soelch 001; Adnan Akhundov; Patrick van der Smagt; Justin Bayer (ICANN (1), 2019)

Unsupervised Real-Time Control Through Variational Empowerment. - Maximilian Karl; Philip Becker-Ehmck; Maximilian Soelch 001; Djalel Benbouzid; Patrick van der Smagt; Justin Bayer (ISRR, 2019)

Approximate Bayesian Inference in Spatial Environments. - Atanas Mirchev; Baris Kayalibay; Maximilian Soelch 001; Patrick van der Smagt; Justin Bayer (Robotics: Science and Systems, 2019)

Variational Tracking and Prediction with Generative Disentangled State-Space Models. - Adnan Akhundov; Maximilian Soelch 001; Justin Bayer; Patrick van der Smagt (CoRR, 2019)

Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models. - Justin Bayer; Maximilian Soelch 001; Atanas Mirchev; Baris Kayalibay; Patrick van der Smagt (ICLR, 2021)

Latent Matters: Learning Deep State-Space Models. - Alexej Klushyn; Richard Kurle; Maximilian Soelch 001; Botond Cseke; Patrick van der Smagt (NeurIPS, 2021)

Uncovering dynamics: Learning and amortized inference for state-space models. - Maximilian Sölch 001 (2021)