This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. With their millions and billions of numerical parameters, deep ...

Artificial intelligence researchers have improved the performance of deep neural networks by combining feature normalization and feature attention modules into a single module that they call attentive ...

Abstract: “In state-of-the-art deep neural networks, both feature normalization and feature attention have become ubiquitous. They are usually studied as separate modules, however. In this paper, we ...

Deep neural networks can perform wonderful feats, thanks to their extremely large and complicated web of parameters. But their complexity is also their curse: The inner workings of neural networks are ...

Many "AI experts" have sprung up in the machine learning space since the advent of ChatGPT and other advanced generative AI constructs late last year, but Dr. James McCaffrey of Microsoft Research is ...

An MIT spinoff co-founded by robotics luminary Daniela Rus aims to build general-purpose AI systems powered by a relatively new type of AI model called a liquid neural network. The spinoff, aptly ...