—Any system which must learn to perform a large number of behavioral features with limited information handling resources will tend to be constrained within a set of architectura...
— We propose a modeling framework based on the event-driven paradigm for populations of neurons which interchange messages. Unlike other strategies our approach is focused on the...
— Our paper presents a fully automated computational mechanism for targeting a space-variant retina based on the highlevel visual content of a scene. Our retina’s receptive fie...
Abstract— In this paper, we propose a new nonlinear principal component analysis based on a generalized correlation function which we call correntropy. The data is nonlinearly tr...
— This work presents a new architecture of artificial neural networks – Venn Networks, which produce localized activations in a 2D map while executing simple cognitive tasks. T...
— This paper shows the advantage of using neural network modularity over conventional learning schemes to approximate complex functions. Indeed, it is difficult for artificial ...
—In this paper we propose a new graph-based feature splitting algorithm maxInd, which creates a balanced split maximizing the independence between the two feature sets. We study ...
— We present a new optimization procedure which is particularly suited for the solution of second-order kernel methods like e.g. Kernel-PCA. Common to these methods is that there...
—The backpropagation algorithm is a very popular approach to learning in feed-forward multi-layer perceptron networks. However, in many scenarios the time required to adequately ...