Gradient descent Taking this performance metric and pushing it back through the network is the backpropagation phase of the learning cycle, and it is the most complex part of the process.

We extend epsilon-subgradient descent methods for unconstrained nonsmooth convex minimization to constrained problems over polyhedral sets, in particular over $\mathbb {R}_ {+}^ {p}$. This is done by ...

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 the '80s, navigating that gradient was derided by MIT scientist Marvin Minsky as mere "hill climbing." (The inverse of gradient descent is like ascending to a summit of highest accuracy.) ...