Thomas Unterthiner
Thomas Unterthiner
Google Research (Brain Team)
Verified email at pm.me
Title
Cited by
Cited by
Year
Fast and accurate deep network learning by exponential linear units (ELUs)
DA Clevert, T Unterthiner, S Hochreiter
International Conference on Learning Representations (ICLR), 2016
39352016
GANs trained by a two time-scale update rule converge to a local nash equilibrium
M Heusel, H Ramsauer, T Unterthiner, B Nessler, S Hochreiter
Advances in Neural Information Processing Systems, 6626-6637, 2017
37272017
Self-normalizing neural networks
G Klambauer, T Unterthiner, A Mayr, S Hochreiter
Advances in Neural Information Processing Systems (NeurIPS), 2017
16812017
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ...
International Conference on Learning Representations (ICLR), 2021
12052021
DeepTox: toxicity prediction using deep learning
A Mayr, G Klambauer, T Unterthiner, S Hochreiter
Frontiers in Environmental Science 3, 80, 2016
5092016
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
A Mayr, G Klambauer, T Unterthiner, M Steijaert, JK Wegner, ...
Chemical science 9 (24), 5441-5451, 2018
2282018
Speeding up Semantic Segmentation for Autonomous Driving
M Treml, J Arjona-Medina, T Unterthiner, R Durgesh, F Friedmann, ...
Workshop on Machine Learning for Intelligent Transportation Systems (NIPS 2016), 2016
1962016
Deep Learning as an Opportunity in Virtual Screening
T Unterthiner, A Mayr, G ünter Klambauer, M Steijaert, J Wenger, ...
Deep Learning and Representation Learning Workshop (NIPS 2014), 2014
1642014
Fréchet ChemNet distance: a metric for generative models for molecules in drug discovery
K Preuer, P Renz, T Unterthiner, S Hochreiter, G Klambauer
Journal of chemical information and modeling 58 (9), 1736-1741, 2018
992018
Object-Centric Learning with Slot Attention
F Locatello, D Weissenborn, T Unterthiner, A Mahendran, G Heigold, ...
Advances in Neural Information Processing Systems (NeurIPS), 2020
922020
Rudder: Return decomposition for delayed rewards
JA Arjona-Medina, M Gillhofer, M Widrich, T Unterthiner, J Brandstetter, ...
Advances in Neural Information Processing Systems (NeurIPS), 2018
892018
Toxicity prediction using deep learning
T Unterthiner, A Mayr, G Klambauer, S Hochreiter
arXiv preprint arXiv:1503.01445, 2015
862015
MLP-Mixer: An All-MLP Architecture for Vision
I Tolstikhin, N Houlsby, A Kolesnikov, L Beyer, X Zhai, T Unterthiner, ...
Advances in Neural Information Processing Systems (NeurIPS), 2021
802021
Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project
B Verbist, G Klambauer, L Vervoort, W Talloen, Z Shkedy, O Thas, ...
Drug discovery today 20 (5), 505-513, 2015
782015
Towards accurate generative models of video: A new metric & challenges
T Unterthiner, S van Steenkiste, K Kurach, R Marinier, M Michalski, ...
arXiv preprint arXiv:1812.01717, 2018
772018
Coulomb GANs: Provably optimal nash equilibria via potential fields
T Unterthiner, B Nessler, G Klambauer, M Heusel, H Ramsauer, ...
International Conference on Learning Representations (ICLR), 2018
552018
Interpretable deep learning in drug discovery
K Preuer, G Klambauer, F Rippmann, S Hochreiter, T Unterthiner
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 331-345, 2019
542019
Fast and accurate deep network learning by exponential linear units (elus)
C Djork-Arné, T Unterthiner, S Hochreiter
arXiv preprint arXiv: 1511.07289, 2015
512015
DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions
G Klambauer, T Unterthiner, S Hochreiter
Nucleic acids research 41 (21), e198-e198, 2013
312013
Deep Learning for Drug Target Prediction
T Unterthiner, A Mayr, G Klambauer, M Steijaert, H Ceulemans, J Wenger, ...
Workshop on Representation and Learning Methods for Complex Outputs (NIPS2014), 2014
30*2014
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