Conventional Deep

Conventional deep neural networks, while powerful, face challenges like high computational cost, oversmoothing in graph-based networks, and resource limitations in edge deployments. Current research focuses on improving efficiency and robustness through novel architectures such as Physics-Informed Kolmogorov-Arnold Networks (PIKAN), Deep Equilibrium (DEQ) models, and resource-efficient subnetworks (REDS), as well as addressing oversmoothing in Graph Convolutional Networks (GCNs) by manipulating weight initialization. These advancements aim to enhance the practicality and reliability of deep learning across diverse applications, from medical image analysis to real-time object detection on resource-constrained devices.