Miej/Dynamic_Neural_Manifold

In this project, I've built a neural network architecture with a static execution graph that acts as a dynamic neural network in which connections between various neurons are controlled by the network itself. This is accomplished by manipulating the adjacency matrix representation of the network on a per-neuron basis with cell elements representing a'distance', and masking off connections that are within a threshold. Including a loss term based on the networks sparsity or processing time allows the architecture to optimize its structure for accuracy or speed. Alright, so hopefully I've caught your attention with the title. To begin, I'd like to explain a little behind why I've created this. My educational background is actually in the sciences, just at the junction between chemistry and physics.