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Thomas Unterthiner
Thomas Unterthiner
Google Research (Brain Team)
Verified email at pm.me
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Cited by
Cited by
Year
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
62722021
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
52632017
Fast and accurate deep network learning by exponential linear units (ELUs)
DA Clevert, T Unterthiner, S Hochreiter
International Conference on Learning Representations (ICLR), 2016
49232016
Self-normalizing neural networks
G Klambauer, T Unterthiner, A Mayr, S Hochreiter
Advances in Neural Information Processing Systems (NeurIPS), 2017
21392017
DeepTox: toxicity prediction using deep learning
A Mayr, G Klambauer, T Unterthiner, S Hochreiter
Frontiers in Environmental Science 3, 80, 2016
6502016
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
4722021
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium.
M Heusel, H Ramsauer, T Unterthiner, B Nessler, G Klambauer, ...
Advances in Neural Information Processing Systems, 2017
3532017
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
3012018
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
2432016
Object-Centric Learning with Slot Attention
F Locatello, D Weissenborn, T Unterthiner, A Mahendran, G Heigold, ...
Advances in Neural Information Processing Systems (NeurIPS), 2020
2362020
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
1842014
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
1492018
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
1322018
Do Vision Transformers See Like Convolutional Neural Networks?
M Raghu, T Unterthiner, S Kornblith, C Zhang, A Dosovitskiy
Advances in Neural Information Processing Systems (NeurIPS), 2021
1302021
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
1262018
Fast and accurate deep network learning by exponential linear units (elus)
C Djork-Arné, T Unterthiner, S Hochreiter
Computer Science, 2015
972015
Toxicity prediction using deep learning
T Unterthiner, A Mayr, G Klambauer, S Hochreiter
arXiv preprint arXiv:1503.01445, 2015
962015
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
882015
Understanding Robustness of Transformers for Image Classification
S Bhojanapalli, A Chakrabarti, D Glasner, D Li, T Unterthiner, A Veit
International Conference on Computer Vision (ICCV), 2021
732021
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
712019
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