| Cloob: Modern hopfield networks with infoloob outperform clip A Fürst, E Rumetshofer, J Lehner, VT Tran, F Tang, H Ramsauer, D Kreil, ... Advances in neural information processing systems 35, 20450-20468, 2022 | 86 | 2022 |
| Accurate prediction of biological assays with high-throughput microscopy images and convolutional networks M Hofmarcher, E Rumetshofer, DA Clevert, S Hochreiter, G Klambauer Journal of chemical information and modeling 59 (3), 1163-1171, 2019 | 80 | 2019 |
| Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks M Hofmarcher, A Mayr, E Rumetshofer, P Ruch, P Renz, J Schimunek, ... arXiv preprint arXiv:2004.00979, 2020 | 59 | 2020 |
| Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns S Kimeswenger, P Tschandl, P Noack, M Hofmarcher, E Rumetshofer, ... Modern Pathology 34 (5), 895-903, 2021 | 30 | 2021 |
| Human-level protein localization with convolutional neural networks E Rumetshofer, M Hofmarcher, C Röhrl, S Hochreiter, G Klambauer International conference on learning representations, 2018 | 23 | 2018 |
| Contrastive learning of image-and structure-based representations in drug discovery A Sanchez-Fernandez, E Rumetshofer, S Hochreiter, G Klambauer ICLR2022 Machine Learning for Drug Discovery, 2022 | 22 | 2022 |
| CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures A Sanchez-Fernandez, E Rumetshofer, S Hochreiter, G Klambauer Nature Communications 14 (1), 7339, 2023 | 10 | 2023 |
| Contrastive tuning: A little help to make masked autoencoders forget J Lehner, B Alkin, A Fürst, E Rumetshofer, L Miklautz, S Hochreiter Proceedings of the AAAI Conference on Artificial Intelligence 38 (4), 2965-2973, 2024 | 5 | 2024 |
| Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images S Kimeswenger, E Rumetshofer, M Hofmarcher, P Tschandl, H Kittler, ... arXiv preprint arXiv:1911.06616, 2019 | 5 | 2019 |
| Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks. 2020 M Hofmarcher, A Mayr, E Rumetshofer, P Ruch, P Renz, J Schimunek, ... DOI: https://doi. org/10.2139/ssrn 3561442, 2004 | 5 | 2004 |
| Contrastive Abstraction for Reinforcement Learning V Patil, M Hofmarcher, E Rumetshofer, S Hochreiter NeurIPS 2023 Workshop on Generalization in Planning, 2023 | | 2023 |
| Learning Retinal Representations from Multi-modal Imaging via Contrastive Pre-training E Sükei, E Rumetshofer, N Schmidinger, U Schmidt-Erfurth, G Klambauer, ... Medical Imaging with Deep Learning, short paper track, 2023 | | 2023 |
| Deep Representation Learning from Weakly Labeled Data/submitted by Elisabeth Rumetshofer E Rumetshofer | | 2023 |
| CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures G Klambauer, AS Fernandez, E Rumetshofer, S Hochreiter | | 2022 |
| End-to-end learning of pharmacological assays from high-resolution microscopy images M Hofmarcher, E Rumetshofer, S Hochreiter, G Klambauer | | 2018 |
| Kartenannotationssystem zur barrierefreien Fußgängernavigation/eingereicht von Elisabeth Rumetshofer E Rumetshofer | | 2017 |
| Accurate Prediction of Biological Assays with High-throughput Microscopy Images and Convolutional Networks—Supporting Information— M Hofmarcher, E Rumetshofer, DA Clevert, S Hochreiter, G Klambauer | | |
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
