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.

news

Sepp Hochreiter is on twitter now!
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

recent publications

  1. JCIM
    Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks
    Seidl, P., Renz, P., Dyubankova, N., Neves, P., Verhoeven, J., Wegner, J., Segler, M., Hochreiter, S., and Klambauer, G.
    Journal of Chemical Information and Modeling 2022
  2. ICML
    Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
    Patil, V., Hofmarcher, M., Dinu, M., Dorfer, M., Blies, P., Brandstetter, J., Arjona-Medina, J., and Hochreiter, S.
    arXiv preprint arXiv:2009.14108 2022
  3. arXiv
    Learning 3D Granular Flow Simulations
    Mayr, A., Lehner, S., Mayrhofer, A., Kloss, C., Hochreiter, S., and Brandstetter, J.
    2021
  4. 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
  5. QSAR
    Benchmarking recent Deep Learning methods on the extended Tox21 data set
    Seidl, P., Halmich, C., Mayr, A., Vall, A., Ruch, P., Hochreiter, S., and Klambauer, G.
    In 2021
  6. Modern Hopfield Networks for Return Decomposition for Delayed Rewards
    Widrich, M., Hofmarcher, M., Patil, V., Bitto-Nemling, A., and Hochreiter, S.
    In Deep RL Workshop NeurIPS 2021 2021
  7. 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
  8. 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
  9. 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
  10. 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