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ORCIDTim Jahn
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2020 – today
- 2024
[i11]Michael Griebel, Tim Jahn:
Efficient solution of ill-posed integral equations through averaging. CoRR abs/2401.16250 (2024)- [i10]Tim Jahn, Bangti Jin:
Early Stopping of Untrained Convolutional Neural Networks. CoRR abs/2402.04610 (2024) - [i9]Johannes Hertrich, Tim Jahn, Michael Quellmalz:
Fast Summation of Radial Kernels via QMC Slicing. CoRR abs/2410.01316 (2024) - 2023
- [j3]Tim Jahn:
Noise Level Free Regularization of General Linear Inverse Problems under Unconstrained White Noise. SIAM/ASA J. Uncertain. Quantification 11(2): 591-615 (2023) - 2022
- [i8]Tim Jahn:
A Probabilistic Oracle Inequality and Quantification of Uncertainty of a modified Discrepancy Principle for Statistical Inverse Problems. CoRR abs/2202.12596 (2022) - [i7]Tim Jahn:
Discretisation-adaptive regularisation of statistical inverse problems. CoRR abs/2204.14037 (2022) - [i6]Tim Jahn:
Noise level free regularisation of general linear inverse problems under unconstrained white noise. CoRR abs/2207.05997 (2022) - 2021
- [b1]Tim Jahn:
Regularising linear inverse problems under unknown non-Gaussian noise. Frankfurt University, Germany, 2021 - [i5]Tim Jahn:
Increasing the relative smoothness of stochastically sampled data. CoRR abs/2103.03545 (2021) - [i4]Tim Jahn:
Optimal Convergence of the Discrepancy Principle for polynomially and exponentially ill-posed Operators under White Noise. CoRR abs/2104.06184 (2021) - 2020
- [j2]Bastian Harrach
, Tim Jahn, Roland Potthast:
Beyond the Bakushinkii veto: regularising linear inverse problems without knowing the noise distribution. Numerische Mathematik 145(3): 581-603 (2020) - [i3]Tim Jahn, Bangti Jin:
On the Discrepancy Principle for Stochastic Gradient Descent. CoRR abs/2004.14625 (2020) - [i2]Bastian Harrach, Tim Jahn, Roland Potthast:
Regularising linear inverse problems under unknown non-Gaussian white noise. CoRR abs/2010.04519 (2020)
2010 – 2019
- 2015
- [j1]Bulcsú Sándor, Tim Jahn, Laura Martin, Claudius Gros:
The Sensorimotor Loop as a Dynamical System: How Regular Motion Primitives May Emerge from Self-Organized Limit Cycles. Frontiers Robotics AI 2: 31 (2015) - [i1]Bulcsú Sándor, Tim Jahn, Laura Martin, Claudius Gros:
The sensorimotor loop as a dynamical system: How regular motion primitives may emerge from self-organized limit cycles. CoRR abs/1511.04338 (2015)
Coauthor Index
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