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Gated Recurrent Unit (GRU)

Gated Recurrent Unit (GRU)

The Gated Recurrent Unit (GRU) is a type of recurrent neural network (RNN) architecture designed to address some of the limitations found in traditional RNNs, particularly the problem of vanishing gradients when dealing with long-term dependencies. Introduced by KyungHyun Cho et al. in their 2014 paper titled "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation," GRUs have become popular due to their simplicity and effectiveness in capturing dependencies in sequential data.

History and Context

The development of GRUs came at a time when researchers were seeking more efficient ways to handle sequential data in tasks like natural language processing, speech recognition, and time series prediction. Before GRUs, Long Short-Term Memory (LSTM) networks were the primary solution for the vanishing gradient problem. However, GRUs were proposed as a less complex alternative, simplifying the architecture while still retaining the ability to learn long-term dependencies:

Architecture

The GRU architecture modifies the traditional RNN by introducing gating mechanisms:

The GRU's mathematical formulation involves these gates:

z_t = σ(W_z * [h_{t-1}, x_t])
r_t = σ(W_r * [h_{t-1}, x_t])
h̃_t = tanh(W * [r_t ⊙ h_{t-1}, x_t])
h_t = (1 - z_t) ⊙ h_{t-1} + z_t ⊙ h̃_t

Where:

Advantages

Applications

GRUs are widely used in:

Limitations

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