AI transforms RF engineering through neural networks that predict signal behavior and interference patterns, enabling ...

Abstract: Understanding how neural networks learn remains one of the central challenges in machine learning research. From random at the start of training, the weights of a neural network evolve in ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

1 KNDS Deutschland GmbH & Co. KG, Munich, Germany 2 Institute for Software Technology, University of the Bundeswehr Munich, Neubiberg, Germany Artificial intelligence (AI) has emerged as a ...

College of Safety Engineering, Chongqing University of Science and Technology, Chongqing 401331, P. R. China ...

SimpleDNN is a machine learning lightweight open-source library written in Kotlin designed to support relevant neural network architectures in natural language processing tasks ...

Leveraging the mapreduce paradigm we propose a solution to parallelize the feedforward operation of neural networks in order to speed it up for sufficiently large NN architectures and for sufficiently ...

Unlike humans, artificial neural networks rapidly forget previously learned information when learning something new and must be retrained by interleaving the new and old items; however, interleaving ...

Abstract: This article proposes a deep neural network (DNN) framework for multivariate deterministic power forecasting in the context of the high penetration of variable and uncertain renewable energy ...