Günter Klambauer
Günter Klambauer
Prof., LIT AI Lab & Institute for Machine Learning, Johannes Kepler University
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GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium
M Heusel, H Ramsauer, T Unterthiner, B Nessler, G Klambauer, ..., 2017
Self-Normalizing Neural Networks
G Klambauer, T Unterthiner, A Mayr, S Hochreiter
Advances in Neural Information Processing Systems 30, 972--981, 2017
DeepTox: toxicity prediction using deep learning
A Mayr, G Klambauer, T Unterthiner, S Hochreiter
Frontiers in Environmental Science 3, 80, 2016
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
cn. MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate
G Klambauer, K Schwarzbauer, A Mayr, DA Clevert, A Mitterecker, ...
Nucleic acids research 40 (9), e69, 2012
Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
F Kratzert, D Klotz, G Shalev, G Klambauer, S Hochreiter, G Nearing
Hydrology and Earth System Sciences 23 (12), 5089-5110, 2019
Hopfield networks is all you need
H Ramsauer, B Schäfl, J Lehner, P Seidl, M Widrich, T Adler, L Gruber, ...
arXiv preprint arXiv:2008.02217, 2020
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
K Preuer, RPI Lewis, S Hochreiter, A Bender, KC Bulusu, G Klambauer
Bioinformatics 34 (9), 1538-1546, 2018
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
Deep learning as an opportunity in virtual screening
T Unterthiner, A Mayr, G Klambauer, M Steijaert, JK Wegner, ...
Deep Learning and Representation Learning Workshop, NIPS 2014, 2014
Repurposed high-throughput image assays enables biological activity prediction for drug discovery
J Simm, G Klambauer, A Arany, M Steijaert, JK Wegner, E Gustin, ...
Cell Chemical Biology, 108399, 2018
Graph networks for molecular design
R Mercado, T Rastemo, E Lindelöf, G Klambauer, O Engkvist, H Chen, ...
Machine Learning: Science and Technology 2 (2), 025023, 2021
How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology
C Wittwehr, H Aladjov, G Ankley, HJ Byrne, J de Knecht, E Heinzle, ...
Toxicological Sciences 155 (2), 326-336, 2017
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
Modern hopfield networks and attention for immune repertoire classification
M Widrich, B Schäfl, M Pavlović, H Ramsauer, L Gruber, M Holzleitner, ...
Advances in neural information processing systems 33, 18832-18845, 2020
Toxicity prediction using deep learning
T Unterthiner, A Mayr, G Klambauer, S Hochreiter
arXiv preprint arXiv:1503.01445, 2015
Prediction of human population responses to toxic compounds by a collaborative competition
F Eduati, LM Mangravite, T Wang, H Tang, S Hochreiter, G Klambauer, ...
Nature biotechnology, 2015
NeuralHydrology–interpreting LSTMs in hydrology
F Kratzert, M Herrnegger, D Klotz, S Hochreiter, G Klambauer
Explainable AI: Interpreting, explaining and visualizing deep learning, 347-362, 2019
On failure modes in molecule generation and optimization
P Renz, D Van Rompaey, JK Wegner, S Hochreiter, G Klambauer
Drug Discovery Today: Technologies 32, 55-63, 2019
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
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