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
32442016
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
23202017
Self-normalizing neural networks
G Klambauer, T Unterthiner, A Mayr, S Hochreiter
Advances in Neural Information Processing Systems (NeurIPS), 2017
13442017
DeepTox: toxicity prediction using deep learning
A Mayr, G Klambauer, T Unterthiner, S Hochreiter
Frontiers in Environmental Science 3, 80, 2016
3942016
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
2592017
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
1722018
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
1522016
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
1492014
Toxicity prediction using deep learning
T Unterthiner, A Mayr, G Klambauer, S Hochreiter
arXiv preprint arXiv:1503.01445, 2015
752015
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
702018
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
662015
An image is worth 16x16 words: Transformers for image recognition at scale
A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ...
arXiv preprint arXiv:2010.11929, 2020
652020
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
652018
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
482018
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
472018
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
382019
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
302013
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
27*2014
Object-centric learning with slot attention
F Locatello, D Weissenborn, T Unterthiner, A Mahendran, G Heigold, ...
arXiv preprint arXiv:2006.15055, 2020
212020
SCIKIT-CUDA 0.5. 1: A Python interface to GPU-powered libraries
LE Givon, T Unterthiner, NB Erichson, DW Chiang, E Larson, L Pfister, ...
21*2015
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Articles 1–20