math.NTMar 31, 2026
Save-adic Character Neural Network
Abstract
We propose a new frame work of -adic neural network. Unlike the original -adic neural network by S.\ Albeverio, A.\ Khrennikov, and B.\ Tirrozi using a family of characteristic functions indexed by hyperparameters of precision as activation functions, we use a single injective -adic character on the topological Abelian group of -adic integers as an activation function. We prove the -adic universal approximation theorem for this formulation of -adic neural network, and reduce it to the feasibility problem of polynomial equations over the finite ring of integers modulo a power of .
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Neural Network Approximation Theory
Neural network approximation theory studies how network size, depth, activations, and structure determine the accuracy of functions and operators.
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