Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing. Deep neural networks are used in a variety of applications, ...

About hCNN: Hybrid Neural Network (Hybrid-NN), a MATLAB NN toolbox that supports complex valued data and insertion of Signal Processing Modules.

A new international study has introduced Curved Neural Networks—a new type of AI memory architecture inspired by ideas from geometry. The study shows that bending the "space" in which AI "thinks" can ...

In this paper, an implementation of a neural network model using systolic arrays, programmed in Verilog Code, is presented. The neural network model is mapped in a three-layer perceptron in forward ...

Neural networks allow for increased e-reading accessibility for education and entertainment products across countless genres. See how, here.

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from ...

The Hypernetwork Field framework introduces a method to model the entire training process of task-specific neural networks, such as DreamBooth, without needing precomputed weights. It uses a ...

A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher. Artificial neurons—the fundamental building blocks of deep neural networks—have survived almost ...

The researchers found that a formula used in statistical analysis provides a streamlined mathematical description of how neural networks, such as GPT-2, a precursor to ChatGPT, learn relevant ...

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.