Efficient Learning of Structured Matrices for Deep Neural Networks
The author proposes a generalized and differentiable framework to learn structured matrices for efficient neural networks, enabling gradient descent optimization. The approach introduces a new format of structured matrices and parameterizes the structure in the frequency domain using the Gaussian-Dirichlet function.
