Follow
Bernhard Schäfl
Bernhard Schäfl
Research Scientist, University of Natural Resources and Life Sciences, Vienna (BOKU)
Verified email at ml.jku.at
Title
Cited by
Cited by
Year
Hopfield networks is all you need
H Ramsauer, B Schäfl, J Lehner, P Seidl, M Widrich, T Adler, L Gruber, ...
International Conference on Learning Representations, 2021
4312021
Modern hopfield networks and attention for immune repertoire classification
M Widrich, B Schäfl, M Pavlović, H Ramsauer, L Gruber, M Holzleitner, ...
Advances in Neural Information Processing Systems 33, 18832-18845, 2020
1142020
Modern Hopfield Networks as Memory for Iterative Learning on Tabular Data
B Schäfl, L Gruber, A Bitto-Nemling, S Hochreiter
Associative Memory & Hopfield Networks in 2023, 2023
20*2023
DeepRC: immune repertoire classification with attention-based deep massive multiple instance learning
M Widrich, B Schäfl, M Pavlović, GK Sandve, S Hochreiter, V Greiff, ...
BioRxiv 2020, 038158, 2020
142020
A GAN based solver of black-box inverse problems
M Gillhofer, H Ramsauer, J Brandstetter, B Schäfl, S Hochreiter
NeurIPS 2019 Workshop on Solving Inverse Problems with Deep Networks, 2019
32019
G-Signatures: Global Graph Propagation With Randomized Signatures
B Schäfl, L Gruber, J Brandstetter, S Hochreiter
arXiv preprint arXiv:2302.08811, 2023
12023
Utilizing Explicit and Implicit Memory in Deep Neural Networks/submitted by Bernhard Franz Schäfl
BF Schäfl
2023
An LSTM-based approach for coiled-coil domain prediction
B Schäfl
Universität Linz, 2018
2018
The system can't perform the operation now. Try again later.
Articles 1–8