Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...

This study evaluates the predictive performance of traditional and machine learning-based models in forecasting NFL team winning percentages over a 21-season dataset (2003–2023). Specifically, we ...

As we progress into 2025, Artificial Intelligence (AI) continues to reshape industries and revolutionize how we interact with technology. For those starting their journey in AI, it’s essential to ...

Deep learning has been highly successful in recent years and has led to dramatic improvements in multiple domains. Deep-learning algorithms often generalize quite well in practice, namely, given ...

Large language models have captured the news cycle, but there are many other kinds of machine learning and deep learning with many different use cases. Amid all the hype and hysteria about ChatGPT, ...

Spiking neural network (SNN) is considered to be the brain-like model that best conforms to the biological mechanism of the brain. Due to the non-differentiability of the spike, the training method of ...

1 Department of Basic Sciences, School of Basic and Biomedical Sciences, University of Health and Allied Sciences, Ho, Ghana. 2 Department of Statistics and Actuarial Science, University of Ghana, ...

Abstract: Conventional artificial neural network (ANN) learning algorithms for classification tasks, either derivative-based optimization algorithms or derivative-free optimization algorithms work by ...

This document describes the process and findings of testing the MAML algorithm for enhancing the convergence of the FxLMS algorithm in noise cancellation applications, specifically targeting aircraft ...