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Frank Schneider
Frank Schneider
Postdoctoral Researcher, University of Tübingen
Verified email at uni-tuebingen.de - Homepage
Title
Cited by
Cited by
Year
Descending through a Crowded Valley -- Benchmarking Deep Learning Optimizers
RM Schmidt, F Schneider, P Hennig
International Conference on Machine Learning, 9367-9376, 2021
1752021
DeepOBS: A Deep Learning Optimizer Benchmark Suite
F Schneider, L Balles, P Hennig
International Conference on Learning Representations, 2019
672019
Benchmarking Neural Network Training Algorithms
GE Dahl, F Schneider, Z Nado, N Agarwal, CS Sastry, P Hennig, ...
arXiv preprint arXiv:2306.07179, 2023
102023
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
R Eschenhagen, A Immer, R Turner, F Schneider, P Hennig
Advances in Neural Information Processing Systems 36, 2024
92024
Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks
F Schneider, F Dangel, P Hennig
Advances in Neural Information Processing Systems 34, 20825-20837, 2021
92021
Late-Phase Second-Order Training
L Tatzel, P Hennig, F Schneider
Has it Trained Yet? NeurIPS 2022 Workshop, 2022
22022
Accelerating Generalized Linear Models by Trading off Computation for Uncertainty
L Tatzel, J Wenger, F Schneider, P Hennig
arXiv preprint arXiv:2310.20285, 2023
12023
Approximations of Inverses of BTTB Matrices
F Schneider
12016
How can we quantify, explain, and apply the uncertainty of complex soil maps predicted with neural networks?
K Rau, K Eggensperger, F Schneider, P Hennig, T Scholten
Science of The Total Environment, 173720, 2024
2024
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
P Hennig, F Schneider, RE Turner, A Immer, R Eschenhagen
arXiv, 2024
2024
What Apples Tell About Oranges: Connecting Pruning Masks and Hessian Eigenspaces
A Fernandez, F Schneider, M Mahsereci, P Hennig
2023
Understanding Deep Learning Optimization via Benchmarking and Debugging
F Schneider
Universität Tübingen, 2022
2022
Methods and apparatus for calculating electromagnetic scattering properties of a structure and for reconstruction of approximate structures
M Pisarenco, FS Schneider, MGMM Van Kraaij, MC Van Beurden
US Patent 11,041,816, 2021
2021
Inverse generating function approach for the preconditioning of Toeplitz‐block systems
FS Schneider, M Pisarenco
Numerical Linear Algebra with Applications, 0
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