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TU Berlin

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Publications

Preprints

  • F. Beier, J. von Lindheim, S. Neumayer, G. Steidl (2021).
    Unbalanced Multi-Marginal Optimal Transport.
    (arXiv Preprint #2103.10854)
    [arxiv]
  • J. Hertrich, S. Neumayer, G. Steidl (2020).
    Convolutional Proximal Neural Networks and Plug-and-Play Algorithms.
    (arXiv Preprint #2011.02281)
    [arxiv]   [Code]
  • P. Koltai, J. von Lindheim, S. Neumayer, G. Steidl (2020).
    Transfer Operators from Optimal Transport Plans for Coherent Set Detection.
    (arXiv Preprint #2006.16085)
    [arxiv]
  • S. Neumayer, G. Steidl (2020).
    From Optimal Transport to Discrepancy.
    Accepted in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging.
    [arxiv]

Journal articles

  • P. Hagemann, S. Neumayer (2021).
    Stabilizing Invertible Neural Networks Using Mixture Models.
    Inverse Problems.
    [doi]   [Code]
  • M. Ehler,  M. Gräf, S. Neumayer, G. Steidl (2021).
    Curve Based Approximation of Measures on Manifolds by Discrepancy Minimization.
    Foundations of Computational Mathematics.
    [doi]
  • C. Hartman, H. A. Weiss, P. Lechner, W. Volk, S. Neumayer, J. H. Fitschen, G. Steidl (2021).
    Measurement of strain, strain rate and crack evolution in shear cutting.
    Journal of Materials Processing Technology. 288:116872.
    [doi]
  • M. Hasannasab, J. Hertrich, S. Neumayer, G. Plonka, S. Setzer, G. Steidl (2020).
    Parseval Proximal Neural Networks.
    Journal of Fourier Analysis and Applications. 26:59.
    [doi]   [Code]
  • A. Effland, S. Neumayer, M. Rumpf (2020).
    Convergence of the Time Discrete Metamorphosis Model on Hadamard Manifolds.
    SIAM Journal on Imaging Sciences. 13(2):557–588.
    [doi]
  • M. Bačák, J. Hertrich, S. Neumayer, G. Steidl (2020).
    Minimal Lipschitz and ∞-Harmonic Extensions of Vector-Valued Functions on Finite Graphs.
    Information and Inference: A Journal of the IMA. 9(4):935-959.
    [doi]   [Code]
  • S. Neumayer,  M. Nimmer, S. Setzer, G. Steidl (2020).
    On the rotational invariant L1-norm PCA.
    Linear Algebra and its Applications. 587:243-270.
    [doi]
  • S. Neumayer,  M. Nimmer, S. Setzer, G. Steidl (2020).
    On the robust PCA and Weiszfeld's algorithm.
    Applied Mathematics and Optimization. 82:1017-1048.
    [doi]
  • L. Lang, S. Neumayer, O. Öktem, C. B. Schönlieb (2020).
    Template-Based Image Reconstruction from Sparse Tomographic Data.
    Applied Mathematics and Optimization. 82:1081-1109.
    [doi]   [Code]
  • S. Neumayer, J. Persch, G. Steidl (2019).
    Regularization of Inverse Problems via Time Discrete Geodesics in Image Spaces.
    Inverse Problems. 35(5):055005.
    [doi]
  • S. Neumayer, J. Persch, G. Steidl (2018).
    Morphing of Manifold-Valued Images inspired by Discrete Geodesics in Image Spaces.
    SIAM Journal on Imaging Sciences. 11(3):1898–1930.
    [doi]

Conference Proceedings

  • J. Lellmann, S. Neumayer, M. Nimmer, G. Steidl, (2019).
    Methods for finding the offset in robust subspace fitting.
    PAMM. 19(1).
    [doi]
  • J. Hertrich, M. Bačák, S. Neumayer, G. Steidl (2019).
    Minimal Lipschitz Extensions for Vector-Valued Functions on Finite Graphs.
    Scale Space and Variational Methods in Computer Vision. Lellmann J., Burger M., Modersitzki J. (eds.) Lecture Notes in Computer Science 11603, pages 183-195.
    [doi]   [Code]
  • S. Neumayer, M. Nimmer, G. Steidl, H. Stephani (2017).
    On a projected Weizfeld algorithm.
    Scale Space and Variational Methods in Computer Vision. Lauze F., Dong Y., Dahl A. (eds.) Lecture Notes in Computer Science 10302, pages 486-497.
    [doi]

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