Vol. 3 No. 1 (2023): Advances in Deep Learning Techniques
Articles

Gated Recurrent Units - Enhancements and Applications: Studying Enhancements to Gated Recurrent Unit (GRU) Architectures and Their Applications in Sequential Modeling Tasks

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  • Dr. Sofia Rodriguez
Dr. Sofia Rodriguez
Assistant Professor of Reinforcement Learning, University of Toronto, Canada
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Published 27-02-2024

Keywords

  • Gated Recurrent Units,
  • GRU,
  • Recurrent Neural Networks,
  • RNN,
  • Sequential Modeling,
  • Enhancements,
  • Applications,
  • Long Short-Term Memory,
  • LSTM,
  • Attention Mechanisms,
  • Initialization Techniques
    • ...More
    Less

How to Cite

[1]
D. S. Rodriguez, “Gated Recurrent Units - Enhancements and Applications: Studying Enhancements to Gated Recurrent Unit (GRU) Architectures and Their Applications in Sequential Modeling Tasks ”, Adv. in Deep Learning Techniques, vol. 3, no. 1, pp. 16–30, Feb. 2024.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

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.

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