Deep Architecture

Deep architectures, encompassing deep neural networks with numerous layers, aim to improve the accuracy and efficiency of machine learning models across diverse applications. Current research focuses on optimizing existing architectures like convolutional neural networks (CNNs) and transformers, exploring techniques such as model compression, early exiting, and novel training strategies to enhance performance and address limitations in resource-constrained environments. These advancements are significant for improving the efficiency and applicability of deep learning in areas like computer vision, natural language processing, and system identification, impacting both scientific understanding and practical deployment of AI systems.