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ORCIDBernhard Nessler
Person information
- affiliation: Johannes Kepler University Linz, Institute of Bioinformatics, Austria
- affiliation: Johann Wolfgang Goethe University, Frankfurt Institute for Advanced Studies (FIAS), Germany
- affiliation: Graz University of Technology, Institute for Theoretical Computer Science, Austria
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2020 – today
- 2023
[i5]Bernhard Nessler, Thomas Doms, Sepp Hochreiter:
Functional trustworthiness of AI systems by statistically valid testing. CoRR abs/2310.02727 (2023)- 2021
- [c6]Werner Zellinger, Natalia Shepeleva, Marius-Constantin Dinu, Hamid Eghbal-zadeh, Hoan Duc Nguyen, Bernhard Nessler, Sergei V. Pereverzyev, Bernhard Alois Moser:
The balancing principle for parameter choice in distance-regularized domain adaptation. NeurIPS 2021: 20798-20811 - [i4]Philip Matthias Winter, Sebastian Eder, Johannes Weissenböck, Christoph Schwald, Thomas Doms, Tom Vogt, Sepp Hochreiter, Bernhard Nessler:
Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications. CoRR abs/2103.16910 (2021)
2010 – 2019
- 2019
- [p1]Markus Hofmarcher, Thomas Unterthiner
, Jose A. Arjona-Medina, Günter Klambauer, Sepp Hochreiter, Bernhard Nessler:
Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation. Explainable AI 2019: 285-296 - [i3]Johannes Lehner, Andreas Mitterecker, Thomas Adler, Markus Hofmarcher, Bernhard Nessler, Sepp Hochreiter:
Patch Refinement - Localized 3D Object Detection. CoRR abs/1910.04093 (2019) - 2018
- [c5]Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter:
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. ICLR (Poster) 2018 - 2017
- [c4]Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter:
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. NIPS 2017: 6626-6637 - [i2]Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, Sepp Hochreiter:
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium. CoRR abs/1706.08500 (2017) - [i1]Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter:
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. CoRR abs/1708.08819 (2017) - 2015
- [j5]Christoph Hartmann, Andreea Lazar, Bernhard Nessler, Jochen Triesch:
Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network. PLoS Comput. Biol. 11(12) (2015) - 2014
- [j4]David Kappel, Bernhard Nessler, Wolfgang Maass:
STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning. PLoS Comput. Biol. 10(3) (2014) - 2013
- [j3]Bernhard Nessler, Michael Pfeiffer, Lars Buesing, Wolfgang Maass:
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity. PLoS Comput. Biol. 9(4) (2013) - 2012
- [c3]Stefan Habenschuss, Johannes Bill, Bernhard Nessler:
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints. NIPS 2012: 782-790 - 2011
- [j2]Lars Buesing, Johannes Bill, Bernhard Nessler, Wolfgang Maass:
Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons. PLoS Comput. Biol. 7(11) (2011) - 2010
- [j1]Michael Pfeiffer, Bernhard Nessler, Rodney J. Douglas, Wolfgang Maass:
Reward-Modulated Hebbian Learning of Decision Making. Neural Comput. 22(6): 1399-1444 (2010)
2000 – 2009
- 2009
- [c2]Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass:
STDP enables spiking neurons to detect hidden causes of their inputs. NIPS 2009: 1357-1365 - 2008
- [c1]Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass:
Hebbian Learning of Bayes Optimal Decisions. NIPS 2008: 1169-1176
Coauthor Index
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last updated on 2023-10-27 19:36 CEST by the dblp team
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