Adaptive method helps light-based quantum processors act more like neural networks

Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language ...
InfoWorld

The most popular neural network styles and how they work

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...
Embedded

Understanding convolutional neural networks

The world of artificial intelligence (AI) is rapidly evolving, and AI is increasingly enabling applications that were previously unattainable or very difficult to implement. A subsequent article, ...
Semiconductor Engineering

Accelerating Inference of Convolutional Neural Networks Using In-memory Computing

“In-memory computing (IMC) is a non-von Neumann paradigm that has recently established itself as a promising approach for energy-efficient, high throughput hardware for deep learning applications. One ...
Semiconductor Engineering

Absence of Barren Plateaus in Quantum Convolutional Neural Networks

Proof of the absence of barren plateaus for a special type of quantum neural network. The work provides trainability guarantees for this architecture, meaning that one can generically train its ...
Scientific Research Publishing

Dual-Dilated Large Kernel Convolution for Visual Attention Network ()

Cheung, K. , Siu, Y. and Chan, K. (2025) Dual-Dilated Large Kernel Convolution for Visual Attention Network. Intelligent ...
Visual Studio Magazine

Convolutional Neural Networks for MNIST Data Using PyTorch

Dr. James McCaffrey of Microsoft Research details the "Hello World" of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset. The "Hello World" of image ...