Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Humans and certain animals appear to have an innate capacity to learn relationships between different objects or events in the world. This ability, known as "relational learning," is widely regarded ...

With artificial intelligence and machine learning (AI/ML) processors and coprocessors roaring across the embedded edge product landscape, the quest continues for high-performance technology that can ...

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 ...

In this video, we will understand forward propagation and backward propagation. Forward propagation and backward propagation ...

A resistor that works in a similar way to nerve cells in the body could be used to build neural networks for machine learning. Many large machine learning models rely on increasing amounts of ...

The initial research papers date back to 2018, but for most, the notion of liquid networks (or liquid neural networks) is a new one. It was “Liquid Time-constant Networks,” published at the tail end ...

As artificial intelligence explodes in popularity, two of its pioneers have nabbed the 2024 Nobel Prize in physics. The prize surprised many, as these developments are typically associated with ...

A hybrid model combining LM, GA, and BP neural networks improves TCM's diagnostic accuracy for IPF, achieving 81.22% ...