For example, gradient descent is often used in machine learning in ways that don’t require extreme precision. But a machine learning researcher might want to double the precision of an experiment. In ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single ...

Ben Grimmer showed that gradient descent algorithms can work faster by including unexpectedly large step sizes — the opposite of what researchers long believed. Will Kirk “It turns out that we did not ...

Dr. James McCaffrey of Microsoft Research explains stochastic gradient descent (SGD) neural network training, specifically implementing a bio-inspired optimization technique called differential ...

Because gradient descent is just a mathematical construct, a geometric model of what's going on in the search for a solution, the entire field of A.I. is only beginning to grasp what the mystery ...

A new publication from Opto-Electronic Advances; DOI 10.29026/oea.2024.230182 , discusses efficient stochastic parallel gradient descent training for on-chip optical processors.