This is a simple implementation of backpropagation using computational graphs. Computational Graphs are directed acyclic graphs that represent mathematical expressions and facilitate the efficient ...

Backpropagation and Gradient Checking is a classic Neural Network problem usedto solve the given computational graph. Given the computational graph first forwardpropagate and predict the class label.

A puzzle that has long flummoxed computers and the scientists who program them has suddenly become far more manageable. A new algorithm efficiently solves the graph isomorphism problem, computer ...

We show that signal flow graph theory provides a simple way to relate two popular algorithms used for adapting dynamic neural networks, real-time backpropagation and backpropagation-through-time.

We propose a class of neural models for graphs that do not rely on backpropagation for training, thus making learning more biologically plausible and amenable to parallel implementation in hardware.

They presented it in June at the ACM Symposium on Theory of Computing, where they detailed an exponentially better method for checking whether a graph is planar. “The new algorithm is a remarkable ...