Go to JKU Homepage
Institute for Machine Learning
What's that?

Institutes, schools, other departments, and programs create their own web content and menus.

To help you better navigate the site, see here where you are at the moment.

Univ. Prof. Mag. Dr. Günter Klambauer

Research Topics

  • Deep Learning & architectures
  • Self-normalizing neural networks & signal propagation theory
  • Machine learning methods for life sciences

Selected Publications

  • Self-Normalizing Neural Networks (2017), Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter. Advances in Neural Information Processing Systems 30, 972--981. [PDF], opens an external URL in a new window
  • DeepTox: toxicity prediction using deep learning (2016), Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Sepp Hochreiter, Frontiers in Environmental Science, 3:80. doi: 10.3389/fenvs.2015.00080, opens an external URL in a new window
  • xLSTM: Extended Long Short-Term Memory (2024. Beck, M., Pöppel, K., Spanring, M., Auer, A., Prudnikova, O., Kopp, M., .., Klambauer, G. Brandstetter, J. & Hochreiter, S. (2024). arXiv preprint arXiv:2405.04517.

Scientific Awards and Competitions

Teaching

  • Deep Learning and neural networks I
  • Deep Learning and neural networks II
  • Artificial Intelligence in life sciences
  • Genome analysis and transcriptomics
  • Sequence analysis and phylogenetics
  • Introduction to machine learning
  • Structural bioinformatics