publications

publications in reverse chronological order.

2022

  1. arXiv
    Hopular: Modern Hopfield Networks for Tabular Data
    Schäfl, B., Gruber, L., Bitto-Nemling, A., and Hochreiter, S.
    2022
  2. 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
  3. ICML
    History Compression via Language Models in Reinforcement Learning
    Paischer, F., Adler, T., Patil, V., Bitto-Nemling, A., Holzleitner, M., Lehner, S., Eghbal-zadeh, H., and Hochreiter, S.
    In 2022
  4. 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
  5. ICLR
    Normalization is dead, long live normalization!
    Hoedt, P., Hochreiter, S., and Klambauer, G.
    In ICLR Blog Track 2022
  6. 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

2021

  1. 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
  2. Modern Hopfield Networks for Return Decomposition for Delayed Rewards
    Widrich, M., Hofmarcher, M., Patil, V., Bitto-Nemling, A., and Hochreiter, S.
    In Deep RL Workshop NeurIPS 2021 2021
  3. 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
  4. 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
  5. arXiv
    Learning 3D Granular Flow Simulations
    Mayr, A., Lehner, S., Mayrhofer, A., Kloss, C., Hochreiter, S., and Brandstetter, J.
    2021
  6. HESSD
    Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling
    Klotz, D., Kratzert, F., Gauch, M., Keefe Sampson, A., Brandstetter, J., Klambauer, G., Hochreiter, S., and Nearing, G.
    Hydrology and Earth System Sciences Discussions 2021
  7. HESS
    A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling
    Kratzert, F., Klotz, D., Hochreiter, S., and Nearing, G.
    Hydrology and Earth System Sciences 2021
  8. HESS
    Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network
    Gauch, M., Kratzert, F., Klotz, D., Nearing, G., Lin, J., and Hochreiter, S.
    Hydrology and Earth System Sciences 2021
  9. Frontiers in AI
    The Promise of AI for DILI Prediction
    Vall, A., Sabnis, Y., Shi, J., Class, R., Hochreiter, S., and Klambauer, G.
    Frontiers in Artificial Intelligence 2021
  10. medRxiv
    Machine Learning based COVID-19 Diagnosis from Blood Tests with Robustness to Domain Shifts
    medRxiv preprint doi:10.1101/2021.04.06.21254997 2021
  11. arXiv
    Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction
    arXiv preprint arXiv:2104.03279 2021
  12. arXiv
    Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications
    Winter, P., Eder, S., Weissenböck, J., Schwald, C., Doms, T., Vogt, T., Hochreiter, S., and Nessler, B.
    2021
  13. QSAR2021
    Comparative assessment of interpretability methods of deep activity models for hERG
    Schimunek, J., Friedrich, L., Kuhn, D., Hochreiter, S., Rippmann, F., and Klambauer, G.
    2021
  14. QSAR
    Benchmarking recent Deep Learning methods on the extended Tox21 data set
    Seidl, P., Halmich, C., Mayr, A., Vall, A., Ruch, P., Hochreiter, S., and Klambauer, G.
    In 2021

2020

  1. WRR
    What Role Does Hydrological Science Play in the Age of Machine Learning?
    Nearing, G., Kratzert, F., Sampson, A., Pelissier, C., Klotz, D., Frame, J., Prieto, C., and Gupta, H.
    Water Resources Research 2020
  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. 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
  6. 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
  7. On Failure Modes in Molecule Generation and Optimization
    Renz, P., Van Rompaey, D., Wegner, J., Hochreiter, S., and Klambauer, G.
    2020
  8. Springer
    Industry-Scale Application and Evaluation of Deep Learning for Drug Target Prediction
    Sturm, N., Mayr, A., Le Van, T., Chupakhin, V., Ceulemans, H., Wegner, J., Golib-Dzib, J., Jeliazkova, N., Vandriessche, Y., Böhm, S., Cima, V., Martinovic, J., Greene, N., Vander Aa, T., Ashby, T., Hochreiter, S., Engkvist, O., Klambauer, G., and Chen, H.
    2020
  9. bioarXiv
    DeepRC: Immune Repertoire Classification with Attention-Based Deep Massive Multiple Instance Learning
    Widrich, M., Schäfl, B., Pavlović, M., Sandve, G., Hochreiter, S., Greiff, V., and Klambauer, G.
    2020
  10. Artificial Neural Networks and Pathologists Recognize Basal Cell Carcinomas Based on Different Histological Patterns
    Kimeswenger, S., Tschandl, P., Noack, P., Hofmarcher, M., Rumetshofer, E., Kindermann, H., Silye, R., Hochreiter, S., Kaltenbrunner, M., Guenova, E., Klambauer, G., and Hoetzenecker, W.

2019

  1. 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
  2. Explaining and Interpreting LSTMs
    Arras, L., Arjona-Medina, J., Widrich, M., Montavon, G., Gillhofer, M., Müller, K., Hochreiter, S., and Samek, W.
    2019
  3. Springer
    Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation
    Hofmarcher, M., Unterthiner, T., Arjona-Medina, J., Klambauer, G., Hochreiter, S., and Nessler, B.
    2019
  4. Detecting Cutaneous Basal Cell Carcinomas in Ultra-High Resolution and Weakly Labelled Histopathological Images
    Kimeswenger, S., Rumetshofer, E., Hofmarcher, M., Tschandl, P., Kittler, H., Hochreiter, S., Hötzenecker, W., and Klambauer, G.
    2019
  5. arXiv
    Patch Refinement - Localized 3D Object Detection
    Lehner, J., Mitterecker, A., Adler, T., Hofmarcher, M., Nessler, B., and Hochreiter, S.
    2019
  6. Machine Learning in Drug Discovery
    Klambauer, G., Hochreiter, S., and Rarey, M.
    2019
  7. WRR
    Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
    Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A., Hochreiter, S., and Nearing, G.
    2019
  8. HESS
    Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine Learning Applied to Large-Sample Datasets
    Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.
    2019
  9. Springer
    NeuralHydrology – Interpreting LSTMs in Hydrology
    Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., and Klambauer, G.
    2019
  10. Community Assessment to Advance Computational Prediction of Cancer Drug Combinations in a Pharmacogenomic Screen
    Menden, M., Wang, D., Mason, M., Szalai, B., Bulusu, K., Guan, Y., Yu, T., Kang, J., Jeon, M., Wolfinger, R., Nguyen, T., Zaslavskiy, M., Jang, I., Ghazoui, Z., Ahsen, M., Vogel, R., Neto, E., Norman, T., Tang, E., Garnett, M., Veroli, G., Fawell, S., Stolovitzky, G., Guinney, J., Dry, J., and Saez-Rodriguez, J.
    2019
  11. Interpretable Deep Learning in Drug Discovery
    Preuer, K., Klambauer, G., Rippmann, F., Hochreiter, S., and Unterthiner, T.
    2019
  12. NeurIPS
    Uncertainty Estimation Methods to Support Decision-Making in Early Phases of Drug Discovery
    Renz, P., Hochreiter, S., and Klambauer, G.
    2019
  13. NeurIPS
    Patch Refinement – Localized 3D Object Detection
    Lehner, J., Mitterecker, A., Adler, T., Hofmarcher, M., Nessler, B., and Hochreiter, S.
    In Workshop on Machine Learning for Autonomous Driving, Neural Information Processing Systems (NeurIPS)
  14. JCIM
    Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks
    Hofmarcher, M., Rumetshofer, E., Clevert, D., Hochreiter, S., and Klambauer, G.

2018

  1. Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
    Unterthiner, T., Nessler, B., Seward, C., Klambauer, G., Heusel, M., Ramsauer, H., and Hochreiter, S.
    2018
  2. Machine Learning in Drug Discovery
    Hochreiter, S., Klambauer, G., and Rarey, M.
    2018
  3. Large-Scale Comparison of Machine Learning Methods for Drug Target Prediction on ChEMBL
    Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J., Ceulemans, H., Clevert, D., and Hochreiter, S.
    2018
  4. DeepSynergy: Predicting Anti-Cancer Drug Synergy with Deep Learning
    Preuer, K., Lewis, R., Hochreiter, S., Bender, A., Bulusu, K., and Klambauer, G.
    2018
  5. Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
    Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S., and Klambauer, G.
    2018
  6. ICLR
    Human-Level Protein Localization with Convolutional Neural Networks
    Rumetshofer, E., Hofmarcher, M., Röhrl, C., Hochreiter, S., and Klambauer, G.
    In 2018
  7. First Order Generative Adversarial Networks
    Seward, C., Unterthiner, T., Bergmann, U., Jetchev, N., and Hochreiter, S.
    2018
  8. Multivariate Analytics of Chromatographic Data: Visual Computing Based on Moving Window Factor Models
    Steinwandter, V., Šišmiš, M., Sagmeister, P., Bodenhofer, U., and Herwig, C.
    2018
  9. Defining Objective Clusters for Rabies Virus Sequences Using Affinity Propagation Clustering
    Fischer, S., Freuling, C., Müller, T., Pfaff, F., Bodenhofer, U., Höper, D., Fischer, M., Marston, D., Fooks, A., Mettenleiter, T., Conraths, F., and Homeier-Bachmann, T.
    2018

2017

  1. Rectified Factor Networks for Biclustering of Omics Data
    Clevert, D., Unterthiner, T., Povysil, G., and Hochreiter, S.
    2017
  2. Learning the High-Dimensional Immunogenomic Features That Predict Public and Private Antibody Repertoires
    Greiff, V., Weber, C., Palme, J., Bodenhofer, U., Miho, E., Menzel, U., and Reddy, S.
    2017
  3. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
    Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S.
    2017
  4. NeurIPS
    Self-Normalizing Neural Networks
    Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S.
    2017
  5. Panelcn.MOPS: Copy-Number Detection in Targeted NGS Panel Data for Clinical Diagnostics
    Povysil, G., Tzika, A., Vogt, J., Haunschmid, V., Messiaen, L., Zschocke, J., Klambauer, G., Hochreiter, S., and Wimmer, K.
    2017

2016

  1. NeurIPS
    Speeding up Semantic Segmentation for Autonomous Driving
    Treml, M., Arjona-Medina, J., Unterthiner, T., Durgesh, R., Friedmann, F., Schuberth, P., Mayr, A., Heusel, M., Hofmarcher, M., Widrich, M., Bodenhofer, U., Nessler, B., and Hochreiter, S.
    In Workshop on Machine Learning for Intelligent Transport Systems, Neural Information Processing Systems (NIPS)

2015

    2014

      2013

        2012