Martin Heusel 个人学术档案

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Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University Linz在 ml.jku.at 的电子邮件经过验证
Thomas UnterthinerGoogle DeepMind在 pm.me 的电子邮件经过验证
Bernhard NesslerSCCH GmbH & Johannes Kepler University Linz, Austria在 scch.at 的电子邮件经过验证
Hinrich WH GöhlmannScientific Director at Johnson & Johnson在 its.jnj.com 的电子邮件经过验证
Willem TalloenDirector, Janssen Pharmaceutcials group在 its.jnj.com 的电子邮件经过验证
Djork-Arné ClevertPfizer, VP, Machine Learning Research在 pfizer.com 的电子邮件经过验证
Ziv SHKEDYProfessor for bioinformatics/biostatostics在 uhasselt.be 的电子邮件经过验证
Andreas Mitterecker在 ml.jku.at 的电子邮件经过验证
Günter KlambauerProf., LIT AI Lab & Institute for Machine Learning, Johannes Kepler University在 ml.jku.at 的电子邮件经过验证
Luc BijnensJanssen (J&J), University Hasselt, Belgium在 UHasselt.be 的电子邮件经过验证
Adetayo S KasimStatistics Director在 gsk.com 的电子邮件经过验证
Ulrich BodenhoferProfessor for Artificial Intelligence, University of Applied Sciences Upper Austria在 fh-hagenberg.at 的电子邮件经过验证
Andreas BenderUniversity of Cambridge; Pangea Bio; Universitatea Babeș-Bolyai在 cam.ac.uk 的电子邮件经过验证
Jörg Kurt WegnerAssociate Scientific Director, Team Lead AI Design Team, Janssen R&D, Cambridge, MA, US在 its.jnj.com 的电子邮件经过验证

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Martin Heusel

Zalando Research

在 zalando.de 的电子邮件经过验证 - 首页

Machine Learning Neural Networks Generative Neural Networks


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	12257	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	387	2010
Speeding up semantic segmentation for autonomous driving M Treml, J Arjona-Medina, T Unterthiner, R Durgesh, F Friedmann, ...	332	2016
Fast model-based protein homology detection without alignment S Hochreiter, M Heusel, K Obermayer Bioinformatics 23 (14), 1728-1736, 2007	185	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	107	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	82	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	59
ELU-networks: Fast and accurate CNN learning on imagenet M Heusel, DA Clevert, G Klambauer, A Mayr, K Schwarzbauer, ... NiN 8, 35-68, 2015	28	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
Generative adversarial networks M Heusel, H Ramsauer, T Unterthiner, B Nessler, S Hochreiter Curran Associates, Inc 30, 6626-6637, 2017	3	2017
Remote Homology detection with LSTM M Heusel		2007
FABIA: Factor Analysis for Bicluster Acquisition—supplementary material— S Hochreiter, U Bodenhofer, M Heusel, A Mayr, A Mitterecker, A Kasim, ...

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