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Found 5 papers, 2 papers with code
Approximation of functions with one-bit neural networks
The celebrated universal approximation theorems for neural networks roughly state that any reasonable function can be arbitrarily well-approximated by a network whose parameters are appropriately chosen real numbers.
Exploring the high dimensional geometry of HSI features
We explore feature space geometries induced by the 3-D Fourier scattering transform and deep neural network with extended attribute profiles on four standard hyperspectral images.
Maximal function pooling with applications
Inspired by the Hardy-Littlewood maximal function, we propose a novel pooling strategy which is called maxfun pooling.
A range characterization of the single-quadrant ADRT
The range characterization is obtained by first showing that the ADRT is a bijection between images supported on infinite half-strips, then identifying the linear subspaces that stay finitely supported under the inversion formula.
Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images
Recent developments in machine learning and signal processing have resulted in many new techniques that are able to effectively capture the intrinsic yet complex properties of hyperspectral imagery.
