Univariate Radial Basis Function Layers: Brain-inspired Deep Neural Layers for Low-Dimensional Inputs
by Daniel Jost, Basavasagar Patil, Xavier Alameda-Pineda, and Chris Reinke [preprint] | [code] Abstract: Deep Neural Networks (DNNs) became the standard tool for function approximation with most of the introduced architectures being developed for high-dimensional input data. However, many real-world problems have low-dimensional inputs for which the standard Multi-Layer Perceptron…