software

software in reverse chronological order.

  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. 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
  3. NeurIPS
    Principled Weight Initialisation for Input-Convex Neural Networks
    Hoedt, P., and Klambauer, G.
    In Advances in Neural Information Processing Systems 2023
  4. 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
  5. 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
  6. 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.
    2022
  7. arXiv
    Hopular: Modern Hopfield Networks for Tabular Data
    Schäfl, B., Gruber, L., Bitto-Nemling, A., and Hochreiter, S.
    2022
  8. 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
  9. 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.
    2022
  10. Toward Semantic History Compression for Reinforcement Learning
    Paischer, F., Adler, T., Radler, A., Hofmarcher, M., and Hochreiter, S.
    2022
  11. DeepRL
    InfODist: Online distillation with Informative rewards improves generalization in Curriculum Learning
    Siripurapu, R., Patil, V., Schweighofer, K., Dinu, M., Schmied, T., Diez, L., Holzleitner, M., Eghbal-zadeh, H., Kopp, M., and Hochreiter, S.
    2022
  12. FMDM
    Foundation Models for History Compression in Reinforcement Learning
    Paischer, F., Adler, T., Radler, A., Hofmarcher, M., and Hochreiter, S.
    2022
  13. 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
  14. arXiv
    CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
    Fürst, A., Rumetshofer, E., Tran, V., Ramsauer, H., Tang, F., Lehner, J., Kreil, D., Kopp, M., Klambauer, G., Bitto-Nemling, A., and Hochreiter, S.
    2021
  15. ICML
    MC-LSTM: Mass-Conserving LSTM
    Hoedt, P., Kratzert, F., Klotz, D., Halmich, C., Holzleitner, M., Nearing, G., Hochreiter, S., and Klambauer, G.
    In Proceedings of the 38th International Conference on Machine Learning 2021
  16. arXiv
    Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning
    Schweighofer, K., Hofmarcher, M., Dinu, M., Renz, P., Bitto-Nemling, A., Patil, V., and Hochreiter, S.
    2021
  17. 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
  18. On Failure Modes in Molecule Generation and Optimization
    Renz, P., Van Rompaey, D., Wegner, J., Hochreiter, S., and Klambauer, G.
    2020
  19. 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
  20. 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
  21. NeurIPS
    RUDDER: Return Decomposition for Delayed Rewards
    Arjona-Medina, J., Gillhofer, M., Widrich, M., Unterthiner, T., Brandstetter, J., and Hochreiter, S.
    In Advances in Neural Information Processing Systems 2019