Bernhard Nessler

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Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
Martin HeuselZalando ResearchVerified email at zalando.de
Thomas UnterthinerGoogle Research (Brain Team)Verified email at pm.me
Hubert RamsauerResearch Assistant at Institute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Michael PfeifferBosch Center for Artificial IntelligenceVerified email at de.bosch.com
Lars BuesingGoogle DeepMindVerified email at google.com
Johannes BillHarvard Medical SchoolVerified email at hms.harvard.edu
Günter KlambauerTT Professor, LIT AI Lab & Institute for Machine Learning, Johannes Kepler UniversityVerified email at bioinf.jku.at
Markus HofmarcherInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Jose A. Arjona-MedinaDynatrace ResearchVerified email at dynatrace.com
David KappelRuhr-Universität BochumVerified email at rub.de
Michael WidrichResearch Assistant at Institute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Andreas MayrInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Calvin SewardZalando ResearchVerified email at zalando.de
Rodney DouglasProfessor of Computational NeuroscienceVerified email at ini.phys.ethz.ch
Andreea LazarErnst Strüngmann Institute for NeuroscienceVerified email at brain.mpg.de
Jochen TrieschFrankfurt Institute for Advanced StudiesVerified email at fias.uni-frankfurt.de
Christoph HartmannSociété des Produits NestléVerified email at rdls.nestle.com
Robert LegensteinInstitute for Theoretical Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at

Bernhard Nessler

SCCH GmbH & Johannes Kepler University Linz, Austria

Verified email at scch.at

Computational Neuroscience Machine Learning Deep Learning Autonomous Driving


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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	5247	2017
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, 6629-6640, 2017	5247	2017
Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons L Buesing, J Bill, B Nessler, W Maass PLoS Computational Biology 7 (11), e1002211, 2011	423	2011
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium. M Heusel, H Ramsauer, T Unterthiner, B Nessler, G Klambauer, ...	352	2017
Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity B Nessler, M Pfeiffer, L Buesing, W Maass PLoS computational biology 9 (4), e1003037, 2013	277	2013
Speeding up Semantic Segmentation for Autonomous Driving M Treml, J Arjona-Medina, T Unterthiner, R Durgesh, F Friedmann, ...	241	2016
STDP enables spiking neurons to detect hidden causes of their inputs B Nessler, M Pfeiffer, W Maass Advances in Neural Information Processing Systems 22, 1357-1365, 2009	125	2009
STDP installs in winner-take-all circuits an online approximation to hidden markov model learning D Kappel, B Nessler, W Maass PLoS computational biology 10 (3), e1003511, 2014	108	2014
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	58	2017
Where’s the noise? key features of spontaneous activity and neural variability arise through learning in a deterministic network C Hartmann, A Lazar, B Nessler, J Triesch PLoS computational biology 11 (12), e1004640, 2015	54	2015
Patch Refinement--Localized 3D Object Detection J Lehner, A Mitterecker, T Adler, M Hofmarcher, B Nessler, S Hochreiter arXiv preprint arXiv:1910.04093, 2019	44	2019
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints S Habenschuss, J Bill, B Nessler Advances in Neural Information Processing Systems, 773-781, 2012	35	2012
Reward-modulated hebbian learning of decision making M Pfeiffer, B Nessler, RJ Douglas, W Maass Neural Computation 22 (6), 1399-1444, 2010	35	2010
Visual scene understanding for autonomous driving using semantic segmentation M Hofmarcher, T Unterthiner, J Arjona-Medina, G Klambauer, ... Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 285-296, 2019	31	2019
Visual scene understanding for autonomous driving using semantic segmentation M Hofmarcher, T Unterthiner, J Arjona-Medina, G Klambauer, ... Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 285-296, 2019	31	2019
Distributed bayesian computation and self-organized learning in sheets of spiking neurons with local lateral inhibition J Bill, L Buesing, S Habenschuss, B Nessler, W Maass, R Legenstein PloS one 10 (8), e0134356, 2015	20	2015
Distributed Bayesian computation and self-organized learning in sheets of spiking neurons with local lateral inhibition J Bill, L Buesing, S Habenschuss, B Nessler, W Maass, R Legenstein PloS one 10 (8), e0134356, 2015	20	2015
Hebbian learning of Bayes optimal decisions B Nessler, M Pfeiffer, W Maass Advances in neural information processing systems, 1169-1176, 2009	15	2009
Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications PM Winter, S Eder, J Weissenböck, C Schwald, T Doms, T Vogt, ... arXiv preprint arXiv:2103.16910, 2021	7	2021
MEDICAL STRIP ELECTRODE J Fortin, W Nessler, B Nessler, F Skrabal EP Patent 1,377,212, 2005	1	2005

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