Search Results for author:
Found 10 papers, 4 papers with code
Functional trustworthiness of AI systems by statistically valid testing
The authors are concerned about the safety, health, and rights of the European citizens due to inadequate measures and procedures required by the current draft of the EU Artificial Intelligence (AI) Act for the conformity assessment of AI systems.
The balancing principle for parameter choice in distance-regularized domain adaptation
Our approach starts with the observation that the widely-used method of minimizing the source error, penalized by a distance measure between source and target feature representations, shares characteristics with regularized ill-posed inverse problems.
Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications
Artificial Intelligence is one of the fastest growing technologies of the 21st century and accompanies us in our daily lives when interacting with technical applications.
Patch Refinement -- Localized 3D Object Detection
We introduce Patch Refinement a two-stage model for accurate 3D object detection and localization from point cloud data.
Ranked #1 on
Object Detection
on KITTI Cars Moderate
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.
Ranked #1 on
Image Generation
on LSUN Bedroom 64 x 64
Speeding up Semantic Segmentation for Autonomous Driving
We propose a novel deep network architecture for image segmentation that keeps the high accuracy while being efficient enough for embedded devices.
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints
Recent spiking network models of Bayesian inference and unsupervised learning frequently assume either inputs to arrive in a special format or employ complex computations in neuronal activation functions and synaptic plasticity rules.
STDP enables spiking neurons to detect hidden causes of their inputs
We show here that STDP, in conjunction with a stochastic soft winner-take-all (WTA) circuit, induces spiking neurons to generate through their synaptic weights implicit internal models for subclasses (or causes") of the high-dimensional spike patterns of hundreds of pre-synaptic neurons.
Hebbian Learning of Bayes Optimal Decisions
Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it.
