Abstract. Modeling the statistical structure of natural images is interesting for reasons related to neuroscience as well as engineering. Currently, this modeling relies heavily on...
Recently the notion of power law networks in the context of neural networks has gathered considerable attention. Some empirical results show that functional correlation networks in...
Polychronization has been proposed as a possible way to investigate the notion of cell assemblies and to understand their role as memory supports for information coding. In a spiki...
Abstract. In this paper, we investigate the application of adaptive ensemble models of Extreme Learning Machines (ELMs) to the problem of one-step ahead prediction in (non)stationa...
Mark van Heeswijk, Yoan Miche, Tiina Lindh-Knuutil...
Abstract. This paper introduces the application of the feature transformation approach proposed by Torkkola [1] to the domain of image processing. Thereto, we extended the approach...
Erik Schaffernicht, Volker Stephan, Horst-Michael ...
Abstract. We demonstrate that selective attention can improve learning. Considerably fewer samples are needed to learn a source separation problem when the inputs are pre-segmented...
In this paper we present neuro-evolution of neural network controllers for mobile agents in a simulated environment. The controller is obtained through evolution of hypercube encod...
Model complexity is key concern to any artificial learning system due its critical impact on generalization. However, EC research has only focused phenotype structural complexity ...
Abstract. This paper’s intention is to present a new approach for decomposing motion trajectories. The proposed algorithm is based on nonnegative matrix factorization, which is a...
In this paper the sparse coding principle is employed for the representation of multimodal image data, i.e. image intensity and range. We estimate an image basis for frontal face i...