"We then developed a quantum thermal gradient descent algorithm, which enables a quantum computer to efficiently find a local ...

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 ...

It's a good spot from which to reflect on the mathematical tool called "stochastic gradient descent," a technique ... called spurious local minima, like a ridge along the way that only looks ...

The last method is adjust(), which takes three arguments. These are values to apply to the weights and bias. It is important to notice that these arguments are subtracted. In gradient descent ...

But the real-world questions that interest mathematicians and scientists are rarely simple. In 1847, the French mathematician Augustin-Louis Cauchy was working on a suitably complicated example — ...

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.