#Introduction Computational graph for backpropagation is a tool used in deep learning to efficiently compute gradients during the training process. It represents the computations performed in a neural ...

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

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.

Just like Backpropagation applies to any differentiable computational graph (and not just a regular multi-layer neural network), Equilibrium Propagation applies to a whole class of energy based models ...