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 with oral talk at MIDL 2023: Learning Retinal Representations from Multi-modal Imaging via Contrastive Pre-training
Paper accepted with oral talk at AAAI 2023: Boundary Graph Neural Networks for 3D Simulations
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

recent publications

  1. arXiv
    Quantification of Uncertainty with Adversarial Models
    Schweighofer, K., Aichberger, L., Ielanski, M., Klambauer, G., and Hochreiter, S.
    2023
  2. arXiv
    SITTA: A Semantic Image-Text Alignment for Image Captioning
    Paischer, F., Adler, T., Hofmarcher, M., and Hochreiter, S.
    2023
  3. arXiv
    Boundary Graph Neural Networks for 3D Simulations
    Mayr, A., Lehner, S., Mayrhofer, A., Kloss, C., Hochreiter, S., and Brandstetter, J.
    2023
  4. arXiv
    Semantic HELM: An Interpretable Memory for Reinforcement Learning
    Paischer, F., Adler, T., Hofmarcher, M., and Hochreiter, S.
    2023
  5. arXiv
    Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget
    Lehner, J., Alkin, B., Fürst, A., Rumetshofer, E., Miklautz, L., and Hochreiter, S.
    2023
  6. RRL
    Learning to Modulate pre-trained Models in RL
    Schmied, T., Hofmarcher, M., Paischer, F., Pascanu, R., and Hochreiter, S.
    2023
  7. MIDL
    Learning Retinal Representations from Multi-modal Imaging via Contrastive Pre-training
    Sükei, E., Rumetshofer, E., Schmidinger, N., Schmidt-Erfurth, U., Klambauer, G., and Bogunović, H.
    In Medical Imaging with Deep Learning, short paper track 2023
  8. AAAI
    Boundary Graph Neural Networks for 3D Simulations
    Mayr, A., Lehner, S., Mayrhofer, A., Kloss, C., Hochreiter, S., and Brandstetter, J.
    Proceedings of the AAAI Conference on Artificial Intelligence 2023
  9. ML4Molecules
    Task-conditioned modeling of drug-target interactions
    Svensson, E., Hoedt, P., Hochreiter, S., and Klambauer, G.
    In ELLIS Machine Learning for Molecule Discovery Workshop 2022
  10. 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.
    2022