(untitled) (Advances in Deep Learning Techniques, 2024)
Gated Recurrent Units (GRUs) have emerged as a powerful variant of recurrent neural networks (RNNs), offering improved learning capabilities for sequential data. This paper presents a comprehensive review of enhancements to GRU architectures and their diverse applications across various domains. We first provide an overview of the standard GRU model and then delve into recent enhancements proposed in the literature. These enhancements include modifications to the gating mechanisms, such as the update gate and reset gate, as well as the incorporation of attention mechanisms and novel initialization techniques. We discuss how these enhancements contribute to overcoming common challenges in RNN training, such as vanishing gradients and capturing long-term dependencies. Furthermore, we explore the applications of enhanced GRU models in natural language processing, time series prediction, speech recognition, and other sequential modeling tasks. Through a series of experiments and case studies, we demonstrate the effectiveness of enhanced GRUs in improving model performance and efficiency. This paper aims to provide researchers and practitioners with a comprehensive understanding of the latest advancements in GRU architectures and their practical implications.