A samplewise backpropagation method for neural networks driven by fractional Brownian motion
The paper develops a fractional stochastic neural network driven by fractional Brownian motion and introduces an adjoint recursion using a discrete stochastic maximum principle. This framework enables the construction of a samplewise gradient estimator, allowing for projected samplewise stochastic gradient descent to achieve mean square convergence for deterministic network parameters.