Two computer scientists found — in the unlikeliest of places — just the idea they needed to make a big leap in graph theory.

University of Virginia School of Engineering and Applied Science professor Nikolaos Sidiropoulos has introduced a breakthrough in graph mining with the development of a new computational algorithm.

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.

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

The 1980s also saw the widespread use of the backpropagation algorithm for training the synaptic weights in both feedforward and recurrent neural networks.

A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI.

This is the case with an important problem in computer science called "graph isomorphism testing" whereby scientists use algorithms to test whether two graphs are the same.

Algorithms are the sets of steps necessary to complete computation - they are at the heart of what our devices actually do. And this isn’t a new concept. Since the development of math itself ...