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

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. arXiv
    MC-LSTM: Mass-Conserving LSTM
    Hoedt, P., Kratzert, F., Klotz, D., Halmich, C., Holzleitner, M., Nearing, G., Hochreiter, S., and Klambauer, G.
    2021
  2. ICLR
    Hopfield Networks Is All You Need
    Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G., Greiff, V., Kreil, D., Kopp, M., Klambauer, G., Brandstetter, J., and Hochreiter, S.
    2020
  3. arXiv
    Cross-Domain Few-Shot Learning by Representation Fusion
    Adler, T., Brandstetter, J., Widrich, M., Mayr, A., Kreil, D., Kopp, M., Klambauer, G., and Hochreiter, S.
    arXiv preprint arXiv:2010.06498 2020
  4. arXiv
    Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER
    Holzleitner, M., Gruber, L., Arjona-Medina, J., Brandstetter, J., and Hochreiter, S.
    2020
  5. arXiv
    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 2020
  6. NeurIPS
    Modern Hopfield networks and attention for immune repertoire classification
    Widrich, M., Schäfl, B., Ramsauer, H., Pavlović, M., Gruber, L., Holzleitner, M., Brandstetter, J., Sandve, G., Greiff, V., Hochreiter, S., and Klambauer, G.
    In Advances in Neural Information Processing Systems 2020
  7. arXiv
    Large-Scale Ligand-Based Virtual Screening for SARS-CoV-2 Inhibitors Using Deep Neural Networks
    Hofmarcher, M., Mayr, A., Rumetshofer, E., Ruch, P., Renz, P., Schimunek, J., Seidl, P., Vall, A., Widrich, M., Hochreiter, S., and Klambauer, G.
    2020
  8. On Failure Modes in Molecule Generation and Optimization
    Renz, P., Van Rompaey, D., Wegner, J., Hochreiter, S., and Klambauer, G.
    2020