Zur JKU Startseite
{{ item.AUTOREN_ZITAT }}:
{{ item.TITEL }}{{ zitatInLang(item) }}
- Science Park 3 - Stockwerk 3 - Raum 309
- +43 732 2468 9399
- +43 732 2468 4539
- klambauer-office(at)ai-lab.jku.at
Forschungsthemen
- Deep Learning & architectures
- Self-normalizing neural networks & signal propagation theory
- Machine learning methods for life sciences
Ausgewählte Publikationen
- Self-Normalizing Neural Networks (2017), Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter. Advances in Neural Information Processing Systems 30, 972--981. [PDF], öffnet eine externe URL in einem neuen Fenster
- 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, öffnet eine externe URL in einem neuen Fenster
- 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.
Wissenschaftliche Wettbewerbe
- 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, öffnet eine externe URL in einem neuen Fenster)
- NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge (2013): Best performing method at the prediction of average cytotoxicity. https://www.synapse.org/#!Synapse:syn1761567/wiki/60840, öffnet eine externe URL in einem neuen Fenster (Team "Austria")
Lehre
- 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
