Abstract Neural networks have emerged as powerful and versatile tools in the field of deep learning. As complexity task increases, so do size architectural network, causing compression techniques to become a focus current research. Parameter truncation can provide significant reduction memory computational complexity. Originating from model order framework, Discrete Empirical Interpolation Meth...

The paper deals with the issue of enhancing communication skills among IT professionals through interactive training methods. rapid evolution education, its constantly changing content, emerging knowledge sectors, and evolving patterns interaction, necessitates urgent attention to this matter. To identify specific needs across various domains, researchers have analyzed regulations, guidelines, ...

Deep neural networks are exposed to the risk of adversarial attacks via fast gradient sign method (FGSM), projected descent (PGD) attacks, and other attack algorithms. Adversarial training is one methods used defend against threat attacks. It a schema that utilizes an alternative objective function provide model generalization for both data clean data. In this systematic review, we focus partic...