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

This repository contains the dataset for the paper "Predicting Software Vulnerability Trends with Multi-Recurrent Neural Networks: A Time Series Forecasting Approach", published in the Proceedings of ...

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 ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

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 ...

in this video, we will understand what is Recurrent Neural Network in Deep Learning. Recurrent Neural Network in Deep Learning is a model that is used for Natural Language Processing tasks. It can be ...

Mathematical analysis of biological neural networks, specifically inhibitory networks with all-to-all connections, is challenging due to their complexity and non-linearity. In examining the dynamics ...

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...

Abstract: This survey explores the potential of recurrent neural networks (RNNs) for analyse eye-trailling data, generally data obtained from Optometry systems. RNNs stand out at distinguishing ...

Abstract: The exploration of quantum advantages with Quantum Neural Networks (QNNs) is an exciting endeavor. Recurrent neural networks, the widely used framework in deep learning, suffer from the ...