AI RESEARCH
Universal Approximation Theorem for Input-Connected Multilayer Perceptrons
arXiv CS.LG
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ArXi:2601.14026v2 Announce Type: replace We present the Input-Connected Multilayer Perceptron (IC-MLP), a feedforward neural network architecture in which each hidden neuron receives, in addition to the outputs of the preceding layer, a direct affine connection from the raw input. We first study this architecture in the univariate setting and give an explicit and systematic description of IC-MLPs with an arbitrary finite number of hidden layers, including iterated formulas for the network functions.