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Learning a Sparse Neural Network using IHT
This paper aims to investigate whether the theoretical prerequisites for such convergence are applicable in the realm of neural network (NN) training by providing justification for all the necessary conditions for convergence.
The Backpropagation algorithm for a math student
A Deep Neural Network (DNN) is a composite function of vector-valued functions, and in order to train a DNN, it is necessary to calculate the gradient of the loss function with respect to all parameters.
Convergence of the mini-batch SIHT algorithm
To the best of our knowledge, in the regime of sparse optimization, this is the first time in the literature that it is shown that the sequence of the stochastic function values converges with probability one by fixing the mini-batch size for all steps.
Amenable Sparse Network Investigator
ASNI-II learns a sparse network and an initialization that is quantized, compressed, and from which the sparse network is trainable.
