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Felix Draxler
Tytuł
Cytowane przez
Cytowane przez
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On the spectral bias of neural networks
N Rahaman, A Baratin, D Arpit, F Draxler, M Lin, F Hamprecht, Y Bengio, ...
International conference on machine learning, 5301-5310, 2019
11072019
Essentially no barriers in neural network energy landscape
F Draxler, K Veschgini, M Salmhofer, F Hamprecht
International conference on machine learning, 1309-1318, 2018
3752018
Framework for easily invertible architectures (FrEIA)
L Ardizzone, T Bungert, F Draxler, U Köthe, J Kruse, R Schmier, ...
Source code, 2018
182018
Whitening convergence rate of coupling-based normalizing flows
F Draxler, C Schnörr, U Köthe
Advances in Neural Information Processing Systems 35, 37241-37253, 2022
72022
Lifting architectural constraints of injective flows
P Sorrenson, F Draxler, A Rousselot, S Hummerich, L Zimmermann, ...
The Twelfth International Conference on Learning Representations, 2023
32023
On the convergence rate of gaussianization with random rotations
F Draxler, L Kühmichel, A Rousselot, J Müller, C Schnörr, U Köthe
International Conference on Machine Learning, 8449-8468, 2023
22023
Characterizing the Role of a Single Coupling Layer in Affine Normalizing Flows
F Draxler, J Schwarz, C Schnörr, U Köthe
DAGM German Conference on Pattern Recognition, 1-14, 2020
22020
On the universality of coupling-based normalizing flows
F Draxler, S Wahl, C Schnörr, U Köthe
arXiv preprint arXiv:2402.06578, 2024
12024
Free-form flows: Make any architecture a normalizing flow
F Draxler, P Sorrenson, L Zimmermann, A Rousselot, U Köthe
arXiv preprint arXiv:2310.16624, 2023
12023
Maximum Likelihood Training of Autoencoders
P Sorrenson, F Draxler, A Rousselot, S Hummerich, L Zimmerman, ...
arXiv preprint arXiv:2306.01843, 2023
12023
Finding competence regions in domain generalization
J Müller, ST Radev, R Schmier, F Draxler, C Rother, U Köthe
arXiv preprint arXiv:2303.09989, 2023
12023
Riemannian SOS-Polynomial Normalizing Flows
J Schwarz, F Draxler, U Köthe, C Schnörr
Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen …, 2021
12021
Bose-Einstein condensate experiment as a nonlinear block of a machine learning pipeline
M Hans, E Kath, M Sparn, N Liebster, H Strobel, MK Oberthaler, F Draxler, ...
Physical Review Research 6 (1), 013122, 2024
2024
Learning Distributions on Manifolds with Free-form Flows
P Sorrenson, F Draxler, A Rousselot, S Hummerich, U Köthe
arXiv preprint arXiv:2312.09852, 2023
2023
Bose Einstein condensate as nonlinear block of a Machine Learning pipeline
M Hans, E Kath, M Sparn, N Liebster, F Draxler, C Schnörr, H Strobel, ...
arXiv preprint arXiv:2304.14905, 2023
2023
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