An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.

This repository provides the implementation of the Virtual Node Graph Neural Network (VGNN) for full phonon prediction in materials science. VGNN is designed to address the challenges in phonon ...

This repository contains working examples of Neural Network Libraries. Before running any of the examples in this repository, you must install the Python package for Neural Network Libraries. The ...

Data Science expert with desire to help companies advance by applying AI for process improvements. This publication provides an in-depth overview of various neural network layers, including their ...

While neural networks used in practice are often very deep, the benefit of depth is not well understood. Interestingly, it is known that increasing depth is often harmful for regression tasks. In this ...

The best way to understand neural networks is to build one for yourself. Let's get started with creating and training a neural network in Java. Artificial neural networks are a form of deep learning ...

Physical scientists and engineering research and development (R&D) teams are embracing neural networks in attempts to accelerate their simulations. From quantum mechanics to the prediction of blood ...

Three different recurrent neural network (RNN) architectures are studied for the prediction of geomagnetic activity. The RNNs studied are the Elman, gated recurrent unit (GRU), and long short-term ...

1 Trumbull High School, Trumbull, USA. 2 University of Chicago/Computer Science, Chicago, USA. External factors, such as social media and financial news, can have wide-spread effects on stock price ...