Nessler, Bernhard

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv

Generative Adversarial Networks and Image SynthesisStochastic Gradient Optimization Techniques

Cited by 8,365

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons

Buesing, L., Bill, J., Nessler, B., & Maass, W. (2011) · PLoS Computational Biology

Neural dynamics and brain functionNeural Networks and Applications

Cited by 384

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

Nessler, B., Pfeiffer, M., Buesing, L., & Maass, W. (2013) · PLoS Computational Biology

Neural dynamics and brain functionNeuroscience and Neuropharmacology Research

Cited by 281

Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation

Hofmarcher, M., Unterthiner, T., Arjona-Medina, J., Klambauer, G., Hochreiter, S., & Nessler, B. (2019) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Cited by 51