Lorenzo Vecci

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In this paper, a new complex-valued neural network based on adaptive activation functions is proposed. By varying the control points of a pair of Catmull–Rom cubic splines, which are used as an adaptable activation function, this new kind of neural network can be implemented as a very simple structure that is able to improve the generalization capabilities(More)
A fast single-pass algorithm to convert a multivalued image from a raster-based representation into chain codes is presented. All chain codes are obtained in linear time with respect to the number of chain segments that are generated at each raster according to a set of templates. A formal statement and the complexity and performance analysis of the(More)
In this paper, we study the theoretical properties of a new kind of artificial neural network, which is able to adapt its activation functions by varying the control points of a Catmull-Rom cubic spline. Most of all, we are interested in generalization capability, and we can show that our architecture presents several advantages. First of all, it can be(More)
In this paper, we study the properties of neural networks based on adaptive spline activation functions (ASNN). Using the results of regularization theory, we show how the proposed architecture is able to produce smooth approximations of unknown functions; to reduce hardware complexity a particular implementation of the kernels expected by the theory is(More)
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