Deriving Ridgelet Transforms for a Variety of Depth-2 Neural Network Architectures Using a Unified Fourier Slice Method

The paper presents a systematic Fourier slice method to derive the ridgelet transform for a variety of modern neural network architectures, including networks on finite fields, group convolutional networks, fully-connected networks on noncompact symmetric spaces, and pooling layers.