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Elisabeth Rumetshofer
Elisabeth Rumetshofer
Institute for Machine Learning, Johannes Kepler University Linz
Email verificata su ml.jku.at
Titolo
Citata da
Citata da
Anno
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
1162022
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
972019
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
632020
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
402021
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
292023
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
272022
Human-level protein localization with convolutional neural networks
E Rumetshofer, M Hofmarcher, C Röhrl, S Hochreiter, G Klambauer
International conference on learning representations, 2018
262018
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
82024
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
72019
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
52004
Contrastive abstraction for reinforcement learning
V Patil, M Hofmarcher, E Rumetshofer, S Hochreiter
arXiv preprint arXiv:2410.00704, 2024
12024
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
12023
Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions
E Sükei, E Rumetshofer, N Schmidinger, A Mayr, U Schmidt-Erfurth, ...
Scientific Reports 14 (1), 26802, 2024
2024
Enriching AI-based Predictive Models from Retinal Imaging by Multi-Modal Contrastive Pre-training
E Sükei, S Riedl, E Rumetshofer, N Schmidinger, A Mayr, ...
Investigative Ophthalmology & Visual Science 65 (7), 450-450, 2024
2024
Improving Clinical Predictions with Multi-Modal Pre-training in Retinal Imaging
E Sükei, E Rumetshofer, N Schmidinger, A Mayr, U Schmidt-Erfurth, ...
2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1-5, 2024
2024
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
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
End-to-end learning of pharmacological assays from high-resolution microscopy images
M Hofmarcher, E Rumetshofer, S Hochreiter, G Klambauer
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20