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Journal Articles
- 2024
[j8]Farid Bozorgnia, Leon Bungert
, Daniel Tenbrinck:
The Infinity Laplacian Eigenvalue Problem: Reformulation and a Numerical Scheme. J. Sci. Comput. 98(2): 40 (2024)- 2023
- [j7]Tim Roith, Leon Bungert:
Continuum Limit of Lipschitz Learning on Graphs. Found. Comput. Math. 23(2): 393-431 (2023) - [j6]Leon Bungert, Philipp Wacker:
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion. SIAM/ASA J. Uncertain. Quantification 11(1): 320-357 (2023) - 2022
- [j5]Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger:
A Bregman Learning Framework for Sparse Neural Networks. J. Mach. Learn. Res. 23: 192:1-192:43 (2022) - 2021
- [j4]Leon Bungert, Ester Hait-Fraenkel, Nicolas Papadakis, Guy Gilboa:
Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks. SIAM J. Imaging Sci. 14(3): 1114-1148 (2021) - 2020
- [j3]Leon Bungert, Matthias J. Ehrhardt:
Robust Image Reconstruction With Misaligned Structural Information. IEEE Access 8: 222944-222955 (2020) - 2018
- [j2]Vadym Aizinger, Leon Bungert, Michael Fried:
Comparison of two local discontinuous Galerkin formulations for the subjective surfaces problem. Comput. Vis. Sci. 18(6): 193-202 (2018) - 2017
- [j1]Leon Bungert, Vadym Aizinger, Michael Fried:
A Discontinuous Galerkin Method for the Subjective Surfaces Problem. J. Math. Imaging Vis. 58(1): 147-161 (2017)
Conference and Workshop Papers
- 2022
- [c4]Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Bjoern M. Eskofier, Dario Zanca:
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification. ICML 2022: 19434-19449 - 2021
- [c3]Leon Bungert, René Raab, Tim Roith, Leo Schwinn, Daniel Tenbrinck:
CLIP: Cheap Lipschitz Training of Neural Networks. SSVM 2021: 307-319 - [c2]Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Björn M. Eskofier:
Identifying untrustworthy predictions in neural networks by geometric gradient analysis. UAI 2021: 854-864 - 2019
- [c1]Leon Bungert, Martin Burger, Daniel Tenbrinck:
Computing Nonlinear Eigenfunctions via Gradient Flow Extinction. SSVM 2019: 291-302
Informal and Other Publications
- 2024
- [i21]Leon Bungert, Tim Laux, Kerrek Stinson:
A mean curvature flow arising in adversarial training. CoRR abs/2404.14402 (2024) - [i20]Leon Bungert, Jeff Calder, Max Mihailescu, Kodjo Houssou, Amber Yuan:
Convergence rates for Poisson learning to a Poisson equation with measure data. CoRR abs/2407.06783 (2024) - 2023
- [i19]Leon Bungert, Nicolás García Trillos, Matt Jacobs, Daniel McKenzie, Dorde Nikolic, Qingsong Wang:
It begins with a boundary: A geometric view on probabilistically robust learning. CoRR abs/2305.18779 (2023) - 2022
- [i18]Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Björn M. Eskofier, Dario Zanca:
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification. CoRR abs/2205.09619 (2022) - [i17]Leon Bungert, Jeff Calder, Tim Roith:
Ratio convergence rates for Euclidean first-passage percolation: Applications to the graph infinity Laplacian. CoRR abs/2210.09023 (2022) - [i16]Leon Bungert, Tim Roith, Philipp Wacker:
Polarized consensus-based dynamics for optimization and sampling. CoRR abs/2211.05238 (2022) - [i15]Leon Bungert, Kerrek Stinson:
Gamma-convergence of a nonlocal perimeter arising in adversarial machine learning. CoRR abs/2211.15223 (2022) - 2021
- [i14]Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Bjoern M. Eskofier:
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis. CoRR abs/2102.12196 (2021) - [i13]Leon Bungert, René Raab, Tim Roith, Leo Schwinn, Daniel Tenbrinck:
CLIP: Cheap Lipschitz Training of Neural Networks. CoRR abs/2103.12531 (2021) - [i12]Leon Bungert, Philipp Wacker:
Complete Dynamics and Spectral Decomposition of the Ensemble Kalman Inversion. CoRR abs/2104.13281 (2021) - [i11]Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger:
A Bregman Learning Framework for Sparse Neural Networks. CoRR abs/2105.04319 (2021) - [i10]Leon Bungert, Martin Burger:
Gradient Flows, Nonlinear Power Methods, and Computation of Nonlinear Eigenfunctions. CoRR abs/2105.08405 (2021) - [i9]Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger:
Neural Architecture Search via Bregman Iterations. CoRR abs/2106.02479 (2021) - [i8]Leon Bungert, Jeff Calder, Tim Roith:
Uniform Convergence Rates for Lipschitz Learning on Graphs. CoRR abs/2111.12370 (2021) - [i7]Leon Bungert, Nicolás García Trillos, Ryan Murray:
The Geometry of Adversarial Training in Binary Classification. CoRR abs/2111.13613 (2021) - 2020
- [i6]Leon Bungert, Matthias J. Ehrhardt:
Robust Image Reconstruction with Misaligned Structural Information. CoRR abs/2004.00589 (2020) - [i5]Farid Bozorgnia, Leon Bungert, Daniel Tenbrinck:
The Infinity Laplacian eigenvalue problem: reformulation and a numerical scheme. CoRR abs/2004.08127 (2020) - [i4]Leon Bungert, Martin Burger, Yury Korolev, Carola-Bibiane Schoenlieb:
Variational regularisation for inverse problems with imperfect forward operators and general noise models. CoRR abs/2005.14131 (2020) - [i3]Tim Roith, Leon Bungert:
Continuum Limit of Lipschitz Learning on Graphs. CoRR abs/2012.03772 (2020) - 2019
- [i2]Leon Bungert, Martin Burger, Daniel Tenbrinck:
Computing Nonlinear Eigenfunctions via Gradient Flow Extinction. CoRR abs/1902.10414 (2019) - 2017
- [i1]Leon Bungert, David A. Coomes, Matthias J. Ehrhardt, Jennifer Rasch, Rafael Reisenhofer, Carola-Bibiane Schönlieb:
Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation. CoRR abs/1710.05705 (2017)
Coauthor Index
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