| 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 | 13545 | 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 | 13545 | 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 | 502 | 2011 |
| Speeding up Semantic Segmentation for Autonomous Driving M Treml, J Arjona-Medina, T Unterthiner, R Durgesh, F Friedmann, ... | 348 | 2016 |
| 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 | 332 | 2013 |
| 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 | 149 | 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 | 132 | 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 | 82 | 2017 |
| Patch Refinement--Localized 3D Object Detection J Lehner, A Mitterecker, T Adler, M Hofmarcher, B Nessler, S Hochreiter arXiv preprint arXiv:1910.04093, 2019 | 69 | 2019 |
| 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 | 67 | 2015 |
| 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 | 67 | |
| 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 | 58 | 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 | 58 | 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 | 40 | 2012 |
| Reward-modulated hebbian learning of decision making M Pfeiffer, B Nessler, RJ Douglas, W Maass Neural Computation 22 (6), 1399-1444, 2010 | 39 | 2010 |
| 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 | 31 | 2021 |
| 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 | 29 | 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 | 29 | 2015 |
| Hebbian learning of Bayes optimal decisions B Nessler, M Pfeiffer, W Maass Advances in neural information processing systems, 1169-1176, 2009 | 19 | 2009 |
| 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 | 12 | 2021 |
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
