Martin Heusel

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Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Thomas UnterthinerGoogle Research (Brain Team)Verified email at pm.me
Bernhard NesslerSCCH GmbH & Johannes Kepler University Linz, AustriaVerified email at scch.at
Hinrich WH GöhlmannScientific Director at Johnson & JohnsonVerified email at its.jnj.com
Willem TalloenDirector, Janssen Pharmaceutcials groupVerified email at its.jnj.com
Djork-Arné ClevertPfizer, VP, Machine Learning ResearchVerified email at pfizer.com
Ziv SHKEDYProfessor for bioinformatics/biostatosticsVerified email at uhasselt.be
Andreas MittereckerVerified email at ml.jku.at
Günter KlambauerAssoc. Prof., LIT AI Lab & Institute for Machine Learning, Johannes Kepler UniversityVerified email at bioinf.jku.at
Luc BijnensJanssen (J&J), University Hasselt, BelgiumVerified email at UHasselt.be
Ulrich BodenhoferProfessor for Artificial Intelligence, University of Applied Sciences Upper AustriaVerified email at fh-hagenberg.at
Andreas BenderProfessor for Molecular Informatics in CambridgeVerified email at cam.ac.uk
Jörg Kurt WegnerAssociate Scientific Director, Team Lead AI Design Team, Janssen R&D, Cambridge, MA, USVerified email at its.jnj.com

Martin Heusel

Zalando Research

Verified email at zalando.de - Homepage

Machine Learning Neural Networks Generative Neural Networks


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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	8047	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	356	2010
Speeding up semantic segmentation for autonomous driving M Treml, J Arjona-Medina, T Unterthiner, R Durgesh, F Friedmann, ...	288	2016
Fast model-based protein homology detection without alignment S Hochreiter, M Heusel, K Obermayer Bioinformatics 23 (14), 1728-1736, 2007	168	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	99	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	78	2017
Gans trained by a two time-scale update rule converge to a local nash equilibrium. arXiv 2017 M Heusel, H Ramsauer, T Unterthiner, B Nessler, S Hochreiter arXiv preprint arXiv:1706.08500, 0	47
ELU-networks: Fast and accurate CNN learning on imagenet M Heusel, DA Clevert, G Klambauer, A Mayr, K Schwarzbauer, ... NiN 8, 35-68, 2015	24	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	8	2012
Remote Homology detection with LSTM M Heusel		2007
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium–Supplementary Material M Heusel, H Ramsauer, T Unterthiner, B Nessler, S Hochreiter
FABIA: Factor Analysis for Bicluster Acquisition—supplementary material— S Hochreiter, U Bodenhofer, M Heusel, A Mayr, A Mitterecker, A Kasim, ...

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