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 NeurIPS 2025: GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations
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

recent publications

  1. NeurIPS
    GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations
    Paischer, F., Galletti, G., Hornsby, W., Setinek, P., Zanisi, L., Carey, N., Pamela, S., and Brandstetter, J.
    In 2025
  2. MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
    Alkin, B., Miklautz, L., Hochreiter, S., and Brandstetter, J.
    arXiv preprint arXiv:2402.10093 2024
  3. Universal Physics Transformers
    Alkin, B., Fürst, A., Schmid, S., Gruber, L., Holzleitner, M., and Brandstetter, J.
    arXiv preprint arXiv:2402.12365 2024
  4. arXiv
    Energy-based hopfield boosting for out-of-distribution detection
    Hofmann, C., Schmid, S., Lehner, B., Klotz, D., and Hochreiter, S.
    arXiv preprint arXiv:2405.08766 2024
  5. Potential predictors for deterioration of renal function after transfusion
    Tschoellitsch, T., Moser, P., Maletzky, A., Seidl, P., Böck, C., Roland, T., Ludwig, H., Süssner, S., Hochreiter, S., and Meier, J.
    Anesthesia & Analgesia 2024
  6. ICML
    A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
    Sanokowski, S., Hochreiter, S., and Lehner, S.
    Proceedings of the 41st International Conference on Machine Learning 2024
  7. arXiv
    Boundary Graph Neural Networks for 3D Simulations
    Mayr, A., Lehner, S., Mayrhofer, A., Kloss, C., Hochreiter, S., and Brandstetter, J.
    2023
  8. NeurIPS
    Variational annealing on graphs for combinatorial optimization
    Sanokowski, S., Berghammer, W., Hochreiter, S., and Lehner, S.
    Advances in Neural Information Processing Systems 2023
  9. Contrastive Abstraction for Reinforcement Learning
    Patil, V., Rumetshofer, E., Hofmarcher, M., and Hochreiter, S.
    In Generlisation in Planning Workshop, NeuRIPS 2023
  10. NeurIPS
    Principled Weight Initialisation for Input-Convex Neural Networks
    Hoedt, P., and Klambauer, G.
    In Advances in Neural Information Processing Systems 2023