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Institute for Machine Learning
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Hubert Ramsauer, MSc

Research Topics

  • Deep Learning and Neural Networks
  • Few-Shot Learning
  • Domain Adaptation 
  • Hopfield Networks
  • Attention Mechanisms
  • Transformers
  • Generative Models

Selected Publications

  • Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Milena Pavlović, Geir Kjetil Sandve, Victor Greiff, David Kreil, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter (2020). Hopfield Networks is All You Need. arXiv preprint arXiv:2008.02217 [PDF, opens an external URL in a new window]
  • Michael Widrich, Bernhard Schäfl, Hubert Ramsauer, Milena Pavlović, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer (2020). Modern Hopfield Networks and Attention for Immune Repertoire Classification. Accepted at 2020 on Conference on Advances in Neural Information Processing Systems. [PDF], opens an external URL in a new window
  • Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter (2017). GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. Accepted at 2017 on Conference on Advances in Neural Information Processing Systems. [PDF], opens an external URL in a new window
  • Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter (2018). Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. Accepted at 2018 on International Conference of Learning Representations. [PDF], opens an external URL in a new window
  • Hubert Ramsauer, Johannes Brandstetter, Michael Gillhofer, Bernhard Schäfl, Sepp Hochreiter (2019). A GAN based solver idea for derivative-free optimization problems. NeurIPS 2019 Workshop: Science meets Engineering of Deep Learning
  • Michael Gillhofer, Hubert Ramsauer, Johannes Brandstetter, Bernhard Schäfl, Sepp Hochreiter (2019). A GAN based solver of black-box inverse problems. NeurIPS 2019 Workshop: Solving inverse problems with deep networks

Teaching

  • 2020: Lecture in LSTM and Recurrent Neural Nets, Co-Lecturer, JKU Linz
  • 2020: Exercises in LSTM and Recurrent Neural Nets, Co-Lecturer, JKU Linz
  • 2019: Lecture in LSTM and Recurrent Neural Nets, Co-Lecturer, JKU Linz
  • 2019: Exercises in LSTM and Recurrent Neural Nets, Co-Lecturer, JKU Linz
  • 2018: Exercises in Theoretical Concepts of Machine Learning, Co-Lecturer, JKU Linz
  • 2018: Exercises in Machine Learning: Unsupervised Techniques, Co-Lecturer, JKU Linz
  • 2017: Lecture in Theoretical Concepts of Machine Learning, Co-Lecturer, JKU Linz
  • 2017: Exercises in Theoretical Concepts of Machine Learning, Co-Lecturer, JKU Linz
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Johannes Kepler University Linz

Altenberger Straße 69

4040 Linz, Austria

Telephone +43 732 2468 0
Fax +43 732 2468 4929
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