A misconception is currently thriving in the industry that one can become a Generative AI expert without learning ...

A new review examines how insertion and deletion (indel) errors disrupt data synchronization in modern communication systems.

Abstract: Unlike traditional feedforward neural networks, recurrent neural networks (RNNs) possess a recurrent connection that allows them to retain past information. This internal memory enables RNNs ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Biologically plausible learning mechanisms have implications for understanding brain functions and engineering intelligent systems. Inspired by the multi-scale recurrent connectivity in the brain, we ...

Abstract: Mobile network traffic prediction is critical for efficient network management in 5G and future 6G systems. Federated Learning (FL) enables multiple base stations (BSs) to collaboratively ...

ABSTRACT: Machine learning (ML) has become an increasingly central component of high-energy physics (HEP), providing computational frameworks to address the growing complexity of theoretical ...

The brain is a highly diverse and heterogeneous network, yet the functional role of this neural heterogeneity remains largely unclear. Despite growing interest in neural heterogeneity, a comprehensive ...

What is a neural network? A neural network, also known as an artificial neural network, is a type of machine learning that works similarly to how the human brain processes information. Instead of ...

The findings of this study are valuable, offering insights into the neural representation of reversal probability in decision-making tasks, with potential implications for understanding flexible ...