Abstract: This article develops a communication-efficient algorithm to solve the stochastic optimization problem defined over a distributed network, aiming at reducing the burdensome communication in ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...

Every day, researchers search for optimal solutions. They might want to figure out where to build a major airline hub. Or to determine how to maximize return while minimizing risk in an investment ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...

No one yet knows how ChatGPT and its artificial-intelligence cousins will transform the world, and one reason is that no one really knows what goes on inside them. Some of these systems’ abilities go ...

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...

Careful psychophysical studies of perception have revealed that neural representations do not encode all aspects of stimuli with equal sensitivity 1. The ability to detect a small change in a stimulus ...

Though we’re living through a time of extraordinary innovation in GPU-accelerated machine learning, the latest research papers frequently (and prominently) feature algorithms that are decades, in ...

1 Department of Statistics, Truman State University, Kirksville, MO, USA. 2 Department of Physics, Virginia Union University, Richmond, VA, USA. 3 Department of ...

One key ingredient in deep learning is the stochastic gradient descent (SGD) algorithm, which allows neural nets to find generalizable solutions at flat minima of the high-dimensional loss function.