On the Construction of Numerical Models through a Prime Convolutional Approach

In this paper we apply neural network models to a set of natural numbers in order to classify the congruence classes modulo a given integer m ∈ {2, 3,..., 10}. We compare the performances of two kinds of architectures and of several input data representations. It turns out that these tasks are fully solved using a convolutional architecture and a special representation for the input data that exploits the prime factor decomposition of numbers.