WiMi's QFNN training algorithm relies on several key quantum computing subroutines, with the most critical components being the quantized feedforward and backpropagation processes.

Modeled on the human brain, neural networks are one of the most common styles of machine learning. Get started with the basic design and concepts of artificial neural networks.

The Forward-Forward algorithm (FF) is comparable in speed to backpropagation but has the advantage that it can be used when the precise details of the forward computation are unknown.

Obtaining the gradient of what's known as the loss function is an essential step to establish the backpropagation algorithm developed by University of Michigan researchers to train a material ...

In early December, dozens of alternatives to traditional backpropagation were proposed during a workshop at the NeurIPS 2020 conference, which took place virtually. Some leveraged hardware like ...

The team designed a fully dynamic APSP algorithm in the MPC model with low round complexity that is faster than all the existing static parallel APSP algorithms.

Based on the gradient projection (GP) method, the new algorithm incorporates a novel multiple-path gradient approach to generate the descent direction in consideration of many paths existing in every ...