Abstract: Neural wirings may be discovered concurrently with training a neural network. Respective weights may be assigned to each edge connecting nodes of a neural graph, wherein the neural graph represents a neural network. A subset of edges may be designated based on the respective weights and data is passed through the neural graph in a forward training pass using the designated subset of edges. A loss function may be determined based on the results of the forward training pass and parameters of the neural network and the respective weights assigned to each edge may be updated in a backwards training pass based on the loss function. The steps of designating the subset of edges, passing data through the neural graph, determining the loss function, and updating parameters of the neural network and the respective weights may be repeated to train the neural network.

Abstract: Neural wirings may be discovered concurrently with training a neural network. Respective weights may be assigned to each edge connecting nodes of a neural graph, wherein the neural graph represents a neural network. A subset of edges may be designated based on the respective weights and data is passed through the neural graph in a forward training pass using the designated subset of edges. A loss function may be determined based on the results of the forward training pass and parameters of the neural network and the respective weights assigned to each edge may be updated in a backwards training pass based on the loss function. The steps of designating the subset of edges, passing data through the neural graph, determining the loss function, and updating parameters of the neural network and the respective weights may be repeated to train the neural network.