software

software in reverse chronological order.

  1. 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
  2. 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
  3. arXiv
    Hopular: Modern Hopfield Networks for Tabular Data
    Schäfl, B., Gruber, L., Bitto-Nemling, A., and Hochreiter, S.
    2022
  4. 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
  5. 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
  6. 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
  7. 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
  8. On Failure Modes in Molecule Generation and Optimization
    Renz, P., Van Rompaey, D., Wegner, J., Hochreiter, S., and Klambauer, G.
    2020
  9. 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
  10. 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
  11. 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
  12. 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