The final, formatted version of the article will be published soon. Robotic automation is a key technology that increases the efficiency and flexibility of manufacturing processes. However, one of the ...
Abstract: Fractional derivatives generalize integer-order derivatives, making them relevant for studying their convergence in descent-based optimization algorithms. However, existing convergence ...
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 the ...
Want your business to show up in Google’s AI-driven results? The same principles that help you rank in Google Search still matter – but AI introduces new dimensions of context, reputation, and ...
DMCN Nash Seeking Based on Distributed Approximate Gradient Descent Optimization Algorithms for MASs
Abstract: A key problem in multiagent multitask systems is optimizing conflict-free strategies, especially when task-assignment is coupled with path-planning. Incomplete information exacerbates this ...
Quantum state tomography (QST) is a widely employed technique for characterizing the state of a quantum system. However, it is plagued by two fundamental challenges: computational and experimental ...
ABSTRACT: The development of artificial intelligence (AI), particularly deep learning, has made it possible to accelerate and improve the processing of data collected in different fields (commerce, ...
A big part of AI and Deep Learning these days is the tuning/optimizing of the algorithms for speed and accuracy. Much of today’s deep learning algorithms involve the use of the gradient descent ...
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