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TU Berlin

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Paul Hagemann

Technische Universität Berlin
Institut für Mathematik
Sekretariat MA 4-3
Straße des 17. Juni 136
10623 Berlin

Raum MA 479


Sekretariat MA 4-3
Julia Wilton
Raum MA 476


J. Hertrich, P. Hagemann and G. Steidl (2021).
A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains.
(arXiv Preprint#2111.12506)

P. Hagemann, J. Hertrich and G. Steidl (2021).
Stochastic Normalizing Flows: a Markov Chains Viewpoint.
(arXiv Preprint:2009.11375)
[arxiv], [Code]

A. Andrle, N. Farchmin, P. Hagemann, S. Heidenreich, V. Soltwisch and G. Steidl (2021).
Invertible Neural Networks Versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence.
Scale Space and Variational Methods in Computer Vision.
Lecture Notes in Computer Science, vol. 12679, pp. 528-539.
[doi], [arxiv]

P. Hagemann, S. Neumayer (2021).
Stabilizing Invertible Neural Networks Using Mixture Models.
Inverse Problems, vol. 37, no. 8.
[doi], [arxiv], [Code]

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