Martin Heusel
Martin Heusel
Johannes Kepler University Linz, Institute for Machine Learning
Verified email at - Homepage
Cited by
Cited by
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
FABIA: factor analysis for bicluster acquisition
S Hochreiter, U Bodenhofer, M Heusel, A Mayr, A Mitterecker, A Kasim, ...
Bioinformatics 26 (12), 1520-1527, 2010
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium.
M Heusel, H Ramsauer, T Unterthiner, B Nessler, G Klambauer, ...
Speeding up semantic segmentation for autonomous driving
M Treml, J Arjona-Medina, T Unterthiner, R Durgesh, F Friedmann, ...
MLITS, NIPS Workshop 2 (7), 2016
Fast model-based protein homology detection without alignment
S Hochreiter, M Heusel, K Obermayer
Bioinformatics 23 (14), 1728-1736, 2007
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
Coulomb GANs: Provably optimal nash equilibria via potential fields
T Unterthiner, B Nessler, C Seward, G Klambauer, M Heusel, ...
arXiv preprint arXiv:1708.08819, 2017
ELU-networks: fast and accurate CNN learning on imagenet
M Heusel, DA Clevert, G Klambauer, A Mayr, K Schwarzbauer, ...
NiN 8, 35-68, 2015
Exploiting the Japanese toxicogenomics project for predictive modelling of drug toxicity
DA Clevert, M Heusel, A Mitterecker, W Talloen, HWH Göhlmann, ...
CAMDA 2012, 26-9, 2012
Remote Homology detection with LSTM
M Heusel
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields Download PDF
TU Thomas, B Nessler, CS Calvin, G Klambauer, M Heusel, H Ramsauer, ...
FABIA: Factor Analysis for Bicluster Acquisition—supplementary material—
S Hochreiter, U Bodenhofer, M Heusel, A Mayr, A Mitterecker, A Kasim, ...
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