Abstract: Deep Neural Networks (DNNs) that aim to maximize accuracy and decrease loss can be trained using optimization algorithms. One of the most significant fields of research is the creation of an ...
In this study, we propose a novel modularized Quantum Neural Network (mQNN) model tailored to address the binary classification problem on the MNIST dataset. The mQNN organizes input information using ...
We explore practical approaches to dataset construction, examining the advantages and limitations of 3 primary methods: fully manual preparation by expert annotators, fully synthetic generation using ...
Natural neural systems have inspired innovations in machine learning and neuromorphic circuits designed for energy-efficient data processing. However, implementing the backpropagation algorithm, a ...
If you’re completely new to Microsoft Word, you’re probably wondering where to begin. You’ve come to the right place because we’ll get you started. From what you see in the Word window to how to save ...
Abstract: Recent studies have demonstrated that deep neural networks show excellent performance in information hiding. Considering the tremendous progress that deep learning has made in image ...
Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware has the potential to significantly reduce the energy consumption of artificial neural network training. SNNs trained with ...
Summary: Researchers developed a neural network that mimics human decision-making by incorporating elements of uncertainty and evidence accumulation. This model, trained on handwritten digits, ...
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