Bernhard Schäfl 个人学术档案

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Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University Linz在 ml.jku.at 的电子邮件经过验证
Milena PavlovićPostdoctoral researcher, University of Oslo在 uio.no 的电子邮件经过验证
Johannes BrandstetterJohannes Kepler University (JKU) Linz在 ml.jku.at 的电子邮件经过验证
Günter KlambauerProf., LIT AI Lab & Institute for Machine Learning, Johannes Kepler University在 ml.jku.at 的电子邮件经过验证
Geir Kjetil SandveProfessor, bioinformatics, University of Oslo在 ifi.uio.no 的电子邮件经过验证
Michael WidrichMachine Learning Scientist @ Freenome在 freenome.com 的电子邮件经过验证
Victor GreiffAssociate Professor, Laboratory for Computational and Systems Immunology, University of Oslo在 medisin.uio.no 的电子邮件经过验证
Hubert RamsauerResearch Assistant at Institute for Machine Learning, Johannes Kepler University Linz在 ml.jku.at 的电子邮件经过验证
Philipp SeidlInstitute for Machine Learning, Johannes Kepler University Linz在 ml.jku.at 的电子邮件经过验证
Thomas AdlerJohannes Kepler University, Linz在 ml.jku.at 的电子邮件经过验证
Johannes LehnerJKU Linz在 ml.jku.at 的电子邮件经过验证
Michael Gillhofer在 ml.jku.at 的电子邮件经过验证
Michael KoppAI researcher

Bernhard Schäfl

Research Scientist, University of Natural Resources and Life Sciences, Vienna (BOKU)

在 ml.jku.at 的电子邮件经过验证


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	463	2021
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	117	2020
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	24*	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	14	2020
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	3	2019
G-Signatures: Global Graph Propagation With Randomized Signatures B Schäfl, L Gruber, J Brandstetter, S Hochreiter arXiv preprint arXiv:2302.08811, 2023	2	2023
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

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