Your browser does not support the audio element. The backpropagation algorithm is the cornerstone of modern artificial intelligence. Its significance goes far beyond ...
ABSTRACT: This paper proposes a unique approach to load forecasting using a fast convergent artificial neural network (ANN) and is driven by the critical need for power system planning. The Mazoon ...
Abstract: Post-training quantization (PTQ) has emerged as a practical approach to compress large neural networks, making them highly efficient for deployment. However, effectively reducing these ...
ABSTRACT: The stock market faces persistent challenges, including inefficiencies, volatility, and barriers to entry, which hinder its accessibility and reliability for investors. This paper explores ...
Obtaining the gradient of what's known as the loss function is an essential step to establish the backpropagation algorithm developed by University of Michigan researchers to train a material. The ...
A new technical paper titled “The backpropagation algorithm implemented on spiking neuromorphic hardware” was published by University of Zurich, ETH Zurich, Los Alamos National Laboratory, Royal ...
Natural neural systems have inspired innovations in machine learning and neuromorphic circuits designed for energy-efficient data processing. However, implementing the backpropagation algorithm, a ...
Recent generations of machine learning, the methodology supporting artificial intelligence, have drawn inspiration from natural neural systems. These algorithmic approaches that mirror the complex ...
Abstract: Artificial intelligence has proliferated across numerous fields of study as a result of its rapid and accurate response times. Its application in fluid dynamics for optimization and ...
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