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- Science Park 3 - Floor 3 - Room 309
- +43 732 2468 9399
- +43 732 2468 4539
- klambauer-office(at)ai-lab.jku.at
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
- Tox21 Data Challenge (2014): Winner of the Grand Challenge, Nuclear Receptor Panel, Stress Response Panel, and six of twelve subchallenges. (Teams "Bioinf@JKU" and "Bioinf@JKU-ensembleX", Method DeepTox, opens an external URL in a new window)
- NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge (2013): Best performing method at the prediction of average cytotoxicity. https://www.synapse.org/#!Synapse:syn1761567/wiki/60840, opens an external URL in a new window (Team "Austria")
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
