Bernhard Nessler

Neuroscience · Life Sciences

17h-index10.1kcitations29works0.02yr avg
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Research Topics

Neural dynamics and brain function(11), Advanced Memory and Neural Computing(8), Neural Networks and Applications(6), Generative Adversarial Networks and Image Synthesis(5), Adversarial Robustness in Machine Learning(4)

Publications29 total

Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
arXiv (Cornell University)·2025Open Access
Functional trustworthiness of AI systems by statistically valid testing
arXiv (Cornell University)·2023Open Access
Künstliche Intelligenz und Deep Learning
Böhlau Verlag eBooks·2022
The balancing principle for parameter choice in distance-regularized domain adaptation
Neural Information Processing Systems·2021· 2 cited
Trusted Artificial Intelligence: Towards Certification of Machine\n Learning Applications
arXiv (Cornell University)·2021· 24 citedOpen Access
Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications
arXiv (Cornell University)·2021· 7 citedOpen Access
Patch Refinement -- Localized 3D Object Detection
arXiv (Cornell University)·2019· 26 citedOpen Access
Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation
Lecture notes in computer science·2019· 46 cited
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
International Conference on Learning Representations·2018· 23 cited
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
arXiv (Cornell University)·2017· 29 citedOpen Access
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium
arXiv (Cornell University)·2017· 371 citedOpen Access
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash\n Equilibrium
arXiv (Cornell University)·2017· 3.8k citedOpen Access
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
arXiv (Cornell University)·2017· 4.5k citedOpen Access
Speeding up Semantic Segmentation for Autonomous Driving
2016· 264 cited
Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network
PLoS Computational Biology·2015· 64 citedOpen Access
Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition
PLoS ONE·2015· 19 citedOpen Access
STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning
PLoS Computational Biology·2014· 117 citedOpen Access
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
PLoS Computational Biology·2013· 293 citedOpen Access
Local inhibition facilitates synaptic learning in spatially extended Bayesian spiking networks
Neuroscience·2013
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints
2012· 30 cited
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Frequent Co-authors

Wolfgang Maass(12), Sepp Hochreiter(11), Thomas Unterthiner(7), Martin Heusel(6), Michael Pfeiffer(6), Hubert Ramsauer(5), Johannes Bill(5), Lars Buesing(4), Günter Klambauer(4), Stefan Habenschuss(4), Markus Hofmarcher(3), Thomas Doms(3), Jose A. Arjona-Medina(2), David Kappel(2), Calvin Seward(2), Philip Matthias Winter(2), Sebastian K. Eder(2), Johannes Weissenböck(2), Christoph Schwald(2), Thomas Vogt(2)