2026-04-13T06:42:06Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4055152026-01-25T02:06:57Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Martínez Gost, Marc author Pérez Neira, Ana Isabel author Lagunas Hernandez, Miguel A. author 2024-03 The expressiveness of neural networks highly depends on the nature of the activation function, although these are usually assumed predefined and fixed during the training stage. Under a signal processing perspective, in this paper we present Expressive Neural Network (ENN), a novel model in which the non-linear activation functions are modeled using the Discrete Cosine Transform (DCT) and adapted using backpropagation during training. This parametrization keeps the number of trainable parameters low, is appropriate for gradient-based schemes, and adapts to different learning tasks. This is the first non-linear model for activation functions that relies on a signal processing perspective, providing high flexibility and expressiveness to the network. We contribute with insights in the explainability of the network at convergence by recovering the concept of bump, this is, the response of each activation function in the output space. Finally, through exhaustive experiments we show that the model can adapt to classification and regression tasks. The performance of ENN outperforms state of the art benchmarks, providing above a 40% gap in accuracy in some scenarios. This work is part of the project IRENE (PID2020-115323RB-C31), funded by MCIN/AEI/10.13039/501100011033. Peer Reviewed Postprint (published version) Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal Signal processing Neural networks (Computer science) Machine learning Adaptive activation functions Discrete cosine transform Explainable machine learning Tractament del senyal Xarxes neuronals (Informàtica) Aprenentatge automàtic ENN: a neural network with DCT adaptive activation functions