85 citations · 114 across the 6 of their papers we have counts for
7 papers
Fast Concept Mapping: The Emergence of Human Abilities in Artificial Neural Networks when Learning Embodied and Self-Supervised
Viviane Clay, Peter König, Gordon Pipa +1
Most artificial neural networks used for object detection and recognition are trained in a fully supervised setup. This is not only very resource consuming as it requires large dat…
WestDrive X LoopAR: An open-access virtual reality project in Unity for evaluating user interaction methods during TOR
Farbod N. Nezami, Maximilian A. Wächter, Nora Maleki +10
With the further development of highly automated vehicles, drivers will engage in non-related tasks while being driven. Still, drivers have to take over control when requested by t…
Reliability and comparability of human brain structural covariance networks
Jona Carmon, Jil Heege, Joe H Necus +5
Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To…
Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks
Leon René Sütfeld, Flemming Brieger, Holger Finger +2
The most widely used activation functions in current deep feed-forward neural networks are rectified linear units (ReLU), and many alternatives have been successfully applied, as w…
Cortical Spike Synchrony as a Measure of Input Familiarity
Clemens Korndörfer, Ekkehard Ullner, Jordi García-Ojalvo +1
Spike synchrony, which occurs in various cortical areas in response to specific perception, action and memory tasks, has sparked a long-standing debate on the nature of temporal or…
Human decisions in moral dilemmas are largely described by Utilitarianism: virtual car driving study provides guidelines for ADVs
Maximilian Alexander Wächter, Anja Faulhaber, Felix Blind +6
Ethical thought experiments such as the trolley dilemma have been investigated extensively in the past, showing that humans act in a utilitarian way, trying to cause as little over…