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Bernhard Nessler
Bernhard Nessler
SCCH GmbH & Johannes Kepler University Linz, Austria
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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
98702017
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
98702017
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
4522011
Speeding up Semantic Segmentation for Autonomous Driving
M Treml, J Arjona-Medina, T Unterthiner, R Durgesh, F Friedmann, ...
3112016
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
3112013
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
1422009
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
1252014
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
782017
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium.
M Heusel, H Ramsauer, T Unterthiner, B Nessler, G Klambauer, ...
64*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
642015
Patch Refinement--Localized 3D Object Detection
J Lehner, A Mitterecker, T Adler, M Hofmarcher, B Nessler, S Hochreiter
arXiv preprint arXiv:1910.04093, 2019
612019
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
502019
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
502019
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
392012
Reward-modulated hebbian learning of decision making
M Pfeiffer, B Nessler, RJ Douglas, W Maass
Neural Computation 22 (6), 1399-1444, 2010
372010
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
272015
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
272015
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
222021
Hebbian learning of Bayes optimal decisions
B Nessler, M Pfeiffer, W Maass
Advances in neural information processing systems, 1169-1176, 2009
182009
The balancing principle for parameter choice in distance-regularized domain adaptation
W Zellinger, N Shepeleva, MC Dinu, H Eghbal-zadeh, HD Nguyen, ...
Advances in Neural Information Processing Systems 34, 20798-20811, 2021
52021
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Artikelen 1–20