Hebbian Learning of Bayes Optimal Decisions. - Bernhard Nessler; Michael Pfeiffer 001; Wolfgang Maass 001 (NIPS, 2008)

STDP enables spiking neurons to detect hidden causes of their inputs. - Bernhard Nessler; Michael Pfeiffer 001; Wolfgang Maass 001 (NIPS, 2009)

Reward-Modulated Hebbian Learning of Decision Making. - Michael Pfeiffer 001; Bernhard Nessler; Rodney J. Douglas; Wolfgang Maass 001 (Neural Comput., 2010)

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons. - Lars Buesing; Johannes Bill; Bernhard Nessler; Wolfgang Maass 001 (PLoS Comput. Biol., 2011)

Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints. - Stefan Habenschuss; Johannes Bill; Bernhard Nessler (NIPS, 2012)

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity. - Bernhard Nessler; Michael Pfeiffer 001; Lars Buesing; Wolfgang Maass 001 (PLoS Comput. Biol., 2013)

STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning. - David Kappel; Bernhard Nessler; Wolfgang Maass 001 (PLoS Comput. Biol., 2014)

Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network. - Christoph Hartmann; Andreea Lazar; Bernhard Nessler; Jochen Triesch (PLoS Comput. Biol., 2015)

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. - Martin Heusel; Hubert Ramsauer; Thomas Unterthiner; Bernhard Nessler; Sepp Hochreiter (NIPS, 2017)

GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium. - Martin Heusel; Hubert Ramsauer; Thomas Unterthiner; Bernhard Nessler; Günter Klambauer; Sepp Hochreiter (CoRR, 2017)

Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. - Thomas Unterthiner; Bernhard Nessler; Günter Klambauer; Martin Heusel; Hubert Ramsauer; Sepp Hochreiter (CoRR, 2017)

Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. - Thomas Unterthiner; Bernhard Nessler; Calvin Seward; Günter Klambauer; Martin Heusel; Hubert Ramsauer; Sepp Hochreiter (ICLR (Poster), 2018)

Patch Refinement - Localized 3D Object Detection. - Johannes Lehner; Andreas Mitterecker; Thomas Adler; Markus Hofmarcher; Bernhard Nessler; Sepp Hochreiter (CoRR, 2019)

Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation. - Markus Hofmarcher; Thomas Unterthiner; Jose A. Arjona-Medina; Günter Klambauer; Sepp Hochreiter; Bernhard Nessler (Explainable AI, 2019)

The balancing principle for parameter choice in distance-regularized domain adaptation. - Werner Zellinger; Natalia Shepeleva; Marius-Constantin Dinu; Hamid Eghbal-zadeh; Hoan Duc Nguyen; Bernhard Nessler; Sergei V. Pereverzyev; Bernhard Alois Moser (NeurIPS, 2021)

Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications. - Philip Matthias Winter; Sebastian Eder; Johannes Weissenböck; Christoph Schwald; Thomas Doms; Tom Vogt; Sepp Hochreiter; Bernhard Nessler (CoRR, 2021)

Functional trustworthiness of AI systems by statistically valid testing. - Bernhard Nessler; Thomas Doms; Sepp Hochreiter (CoRR, 2023)

Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind. - Mouad Abrini; Omri Abend; Dina M. Acklin; Henny Admoni; Gregor Aichinger; Nitay Alon; Zahra Ashktorab; Ashish Atreja; Moises Auron; Alexander Aufreiter; Raghav Awasthi; Soumya Banerjee; et al. (CoRR, 2025)