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.


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. CoLLAs
    Few-Shot Learning by Dimensionality Reduction in Gradient Space
    Gauch, M., Beck, M., Adler, T., Kotsur, D., Fiel, S., Eghbal-zadeh, H., Brandstetter, J., Kofler, J., Holzleitner, M., Zellinger, W., Klotz, D., Hochreiter, S., and Lehner, S.
  2. 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
  3. 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
  4. ICLR
    Normalization is dead, long live normalization!
    Hoedt, P., Hochreiter, S., and Klambauer, G.
    In ICLR Blog Track 2022
  5. CoLLAs
    Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning
    Steinparz, C., Schmied, T., Paischer, F., Dinu, M., Patil, V., Bitto-Nemling, A., Eghbal-zadeh, H., and Hochreiter, S.
  6. arXiv
    Hopular: Modern Hopfield Networks for Tabular Data
    Schäfl, B., Gruber, L., Bitto-Nemling, A., and Hochreiter, S.
  7. ICML
    History Compression via Language Models in Reinforcement Learning
    Paischer, F., Adler, T., Patil, V., Bitto-Nemling, A., Holzleitner, M., Lehner, S., Eghbal-zadeh, H., and Hochreiter, S.
    In 2022
  8. CoLLAs
    A Dataset Perspective on Offline Reinforcement Learning
    Schweighofer, K., Radler, A., Dinu, M., Hofmarcher, M., Patil, V., Bitto-Nemling, A., Eghbal-zadeh, H., and Hochreiter, S.
  9. 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
  10. 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