GOES-East satellite observations and machine learning have, for the first time, connected this observed structure to the much ...
In recent years, artificial intelligence has become more accessible than ever before. Powerful libraries, automated platforms ...
Abstract: A crucial task in predictive maintenance is estimating the remaining useful life of physical systems. In the last decade, deep learning has improved considerably upon traditional model-based ...
Those changes will be contested, in math as in other academic disciplines wrestling with AI’s impact. As AI models become a ...
Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you’ve ever built a predictive model, worked on a ...
High-temperature proton exchange membrane fuel cells (HT-PEMFCs) are highly promising for next-generation aviation, as they can operate above 160 °C and tolerate impurities in the fuel. However, they ...
ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
Abstract: Online first-order algorithms for function identification and regression with noisy data often rely on replacing actual gradients with their constructed noisy estimates. Stochastic gradient ...
This repository explores the concept of Orthogonal Gradient Descent (OGD) as a method to mitigate catastrophic forgetting in deep neural networks during continual learning scenarios. Catastrophic ...
ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...
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