THE EVOLUTION OF RECURRENT NEURAL NETWORKS IN HANDLING LONG-TERM DEPENDENCIES IN SEQUENTIAL DATA
Keywords:
Recurrent Neural Networks, Long-Term Dependencies, Sequential Data, LSTM, GRU, Attention Mechanisms, Transformer Models, Vanishing Gradient Problem, Time-Series Forecasting, Natural Language ProcessingAbstract
Recurrent Neural Networks (RNNs) have undergone significant evolution in their ability to handle long-term dependencies in sequential data. This paper reviews the development of RNN architectures, from early vanilla models to more advanced structures like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). The challenges associated with capturing long-term dependencies, particularly the vanishing and exploding gradient problems, are discussed, along with the innovations that have overcome these limitations. The paper also examines recent advancements such as attention mechanisms and transformer models, which have further enhanced the capability of RNNs. A comparative analysis of these architectures is provided, demonstrating their effectiveness in various applications, including natural language processing, time-series forecasting, and speech recognition. The findings highlight the continued relevance of RNNs in sequential data tasks while pointing to future research directions that could further improve their performance.
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Copyright (c) 2024 K K Ramachandran (Author)
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