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ORCIDMaximilian Soelch
Person information
- affiliation: argmax.ai, Munich, Germany
- affiliation: Volkswagen Group Machine Learning Research Lab, Munich, Germany
- affiliation (former): TU Munich, Department of Informatics, Germany
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Books and Theses
- 2021
[b1]Maximilian Sölch:
Uncovering dynamics: Learning and amortized inference for state-space models. Technical University of Munich, Germany, 2021
Conference and Workshop Papers
- 2023
- [c7]Stefanie Manger, Maximilian Sölch, Matthias Linhuber, Christoph Weinhuber, Philipp Zagar, Stephan Krusche:
Is Online Teaching Dead After COVID-19? Student Preferences for Programming Courses. CSEE&T 2023: 89-98 - 2021
- [c6]Justin Bayer, Maximilian Soelch, Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt
:
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models. ICLR 2021 - [c5]Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt:
Latent Matters: Learning Deep State-Space Models. NeurIPS 2021: 10234-10245 - 2019
- [c4]Maximilian Soelch, Adnan Akhundov, Patrick van der Smagt, Justin Bayer:
On Deep Set Learning and the Choice of Aggregations. ICANN (1) 2019: 444-457 - [c3]Maximilian Karl, Philip Becker-Ehmck, Maximilian Soelch, Djalel Benbouzid, Patrick van der Smagt, Justin Bayer:
Unsupervised Real-Time Control Through Variational Empowerment. ISRR 2019: 158-173 - [c2]Atanas Mirchev, Baris Kayalibay, Maximilian Soelch, Patrick van der Smagt, Justin Bayer:
Approximate Bayesian Inference in Spatial Environments. Robotics: Science and Systems 2019 - 2017
- [c1]Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt:
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data. ICLR (Poster) 2017
Informal and Other Publications
- 2023
- [i6]Maximilian Sölch, Moritz Aberle, Stephan Krusche:
Integrating Competency-Based Education in Interactive Learning Systems. CoRR abs/2309.12343 (2023) - 2021
- [i5]Justin Bayer, Maximilian Soelch, Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt:
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models. CoRR abs/2101.07046 (2021) - 2019
- [i4]Maximilian Soelch, Adnan Akhundov, Patrick van der Smagt, Justin Bayer:
On Deep Set Learning and the Choice of Aggregations. CoRR abs/1903.07348 (2019) - [i3]Adnan Akhundov, Maximilian Soelch, Justin Bayer, Patrick van der Smagt:
Variational Tracking and Prediction with Generative Disentangled State-Space Models. CoRR abs/1910.06205 (2019) - 2016
- [i2]Maximilian Soelch, Justin Bayer, Marvin Ludersdorfer, Patrick van der Smagt:
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series. CoRR abs/1602.07109 (2016) - [i1]Maximilian Karl, Maximilian Soelch, Justin Bayer, Patrick van der Smagt:
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data. CoRR abs/1605.06432 (2016)
Coauthor Index
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