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Martin Burger 0001
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Publications
- 2022
- [j64]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
- [c20]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 - [i30]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) - [i29]Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger:
A Bregman Learning Framework for Sparse Neural Networks. CoRR abs/2105.04319 (2021) - [i28]Leon Bungert, Martin Burger:
Gradient Flows, Nonlinear Power Methods, and Computation of Nonlinear Eigenfunctions. CoRR abs/2105.08405 (2021) - [i27]Leon Bungert, Tim Roith, Daniel Tenbrinck, Martin Burger:
Neural Architecture Search via Bregman Iterations. CoRR abs/2106.02479 (2021) - 2020
- [i23]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) - 2019
- [c19]Leon Bungert, Martin Burger, Daniel Tenbrinck:
Computing Nonlinear Eigenfunctions via Gradient Flow Extinction. SSVM 2019: 291-302 - [i17]Leon Bungert, Martin Burger, Daniel Tenbrinck:
Computing Nonlinear Eigenfunctions via Gradient Flow Extinction. CoRR abs/1902.10414 (2019)
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