A Comprehensive Review of Gated Architectures in Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have been widely used in various applications such as natural language processing, speech recognition, and time series analysis due to their ability to capture temporal dependencies in data. However, traditional RNNs suffer from the vanishing gradient problem, which limits their ability to capture long-range dependencies in sequential data.

To address this issue, a new class of RNN architectures called Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells have been introduced. These architectures incorporate gating mechanisms that allow the network to selectively update and forget information based on the input data, enabling them to better capture long-range dependencies.

In this article, we will provide a comprehensive review of gated architectures in RNNs, focusing on the GRU and LSTM cells. We will discuss how these architectures work, their advantages and disadvantages, and their applications in various domains.

Gated Recurrent Units (GRUs) are a simplified version of LSTM cells that have been shown to perform comparably to LSTMs in many tasks. GRUs have two gating mechanisms – an update gate and a reset gate – that control the flow of information through the network. The update gate determines how much of the previous hidden state should be retained, while the reset gate determines how much of the new input should be incorporated into the current hidden state.

One of the advantages of GRUs is that they are computationally more efficient than LSTMs, as they have fewer parameters and require fewer computations. This makes them ideal for applications where computational resources are limited.

On the other hand, Long Short-Term Memory (LSTM) cells are more complex than GRUs and have three gating mechanisms – an input gate, a forget gate, and an output gate. The input gate controls how much of the new input should be incorporated into the current hidden state, the forget gate determines how much of the previous hidden state should be retained, and the output gate determines how much of the current hidden state should be outputted.

LSTMs have been shown to excel in tasks that require capturing long-range dependencies, such as machine translation and speech recognition. However, they are also more computationally expensive than GRUs due to their higher number of parameters and computations.

In conclusion, gated architectures such as GRUs and LSTMs have revolutionized the field of recurrent neural networks by addressing the vanishing gradient problem and enabling the networks to capture long-range dependencies in sequential data. While both architectures have their own strengths and weaknesses, they have been successfully applied in a wide range of applications and continue to be a topic of active research in the deep learning community.


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