The Institute for Machine Learning conducts research and provides profound education in machine learning. Its research focuses on development of machine learning and statistical methods. We further apply these methods to various domains like medicine, drug discovery, autonomous driving, earth science, natural language processing, control and others. The institute is led by Sepp Hochreiter and is affiliated with Johannes Kepler University Linz and is also one of the founding units of European Lab for Learning and Intelligent Systems (ELLIS).

The Institute for Machine Learning is located in the beautiful city of Linz, Austria. Located near the amazing Austrian alps, the once European capital of culture mixes the right balance between nature and urban life. A part of the institute is also located in the city of Vienna, Austria.


Paper accepted at ICLR 2021: Hopfield Networks is all you need
Talk by Sepp Hochreiter on the topic of Modern Hopfield networks and Dense associative memories: link
New blog post on the relationship of Performers and Hopfield Networks: link
New blog post on Cross domain few shot learning: link
Paper accepted with spotlight presentation at NeurIPS 2020: Modern Hopfield Networks and Attention for Immune Repertoire Classification

recent publications

  1. QSAR2021
    Benchmarking recent Deep Learning methods on the extended Tox21 data set
    Seidl, P., Christina, H., Mayr, A., Vall, A., Ruch, P., Hochreiter, S., and Klambauer, G.
  2. HESS
    A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling
    Kratzert, F., Klotz, D., Hochreiter, S., and Nearing, G.
    Hydrology and Earth System Sciences 2021
  3. arXiv
    CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
    Fürst, A., Rumetshofer, E., Tran, V., Ramsauer, H., Tang, F., Lehner, J., Kreil, D., Kopp, M., Klambauer, G., Bitto-Nemling, A., and Hochreiter, S.
  4. HESS
    Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network
    Gauch, M., Kratzert, F., Klotz, D., Nearing, G., Lin, J., and Hochreiter, S.
    Hydrology and Earth System Sciences 2021
  5. Frontiers in AI
    The Promise of AI for DILI Prediction
    Vall, A., Sabnis, Y., Shi, J., Class, R., Hochreiter, S., and Klambauer, G.
    Frontiers in Artificial Intelligence 2021
  6. medRxiv
    Machine Learning based COVID-19 Diagnosis from Blood Tests with Robustness to Domain Shifts
    medRxiv preprint doi:10.1101/2021.04.06.21254997 2021
  7. arXiv
    Learning 3D Granular Flow Simulations
    Mayr, A., Lehner, S., Mayrhofer, A., Kloss, C., Hochreiter, S., and Brandstetter, J.
  8. HESSD
    Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling
    Klotz, D., Kratzert, F., Gauch, M., Keefe Sampson, A., Brandstetter, J., Klambauer, G., Hochreiter, S., and Nearing, G.
    Hydrology and Earth System Sciences Discussions 2021
  9. arXiv
    Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction
    arXiv preprint arXiv:2104.03279 2021
  10. arXiv
    Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications
    Winter, P., Eder, S., Weissenböck, J., Schwald, C., Doms, T., Vogt, T., Hochreiter, S., and Nessler, B.