Fully Connected

Fully-connected networks (FCNs), a fundamental architecture in deep learning, are the subject of ongoing research aimed at understanding their capabilities and limitations, and improving their efficiency and performance. Current research focuses on analyzing FCNs' theoretical properties, particularly concerning their ability to learn complex functions and generalize well, often comparing them to alternative architectures like transformers. This research is significant because it helps clarify the theoretical underpinnings of deep learning, leading to the development of more efficient and robust algorithms with applications across diverse fields, including resource allocation in dynamic networks and improved control systems.