MIT researchers have developed a new machine-learning-based adaptive control algorithm for autonomous drones. The ...

An autonomous drone carrying water to help extinguish a wildfire in the Sierra Nevada might encounter swirling Santa Ana ...

Gradient descent algorithms take the loss function and use partial derivatives to determine what each variable (weights and biases) in the network contributed to the loss value. It then moves ...

The most widely used technique for finding the largest or smallest values of a math function turns out to be a fundamentally difficult computational problem. Many aspects of modern applied research ...

Optimization methods for machine learning, including neural networks, typically use some form of gradient descent algorithm to drive the back propagation, often with a mechanism to help avoid ...

All answers will be at display at the official Algorithm Hall of Fame, but for now we’re giving you the exclusive. Siraj Raval: “I believe the gradient descent algorithm has had the biggest ...

In 1847, the French mathematician Augustin-Louis Cauchy was working on a suitably complicated example — astronomical calculations — when he pioneered a common method of optimization now known as ...

The algorithm creates a population of eight possible solutions ... DEO typically takes much longer to train a deep neural network than standard stochastic gradient descent (SGD) optimization ...