Gated Recurrent Units

GRU is the acronym for Gated Recurrent Units.

A type of recurrent neural network (RNN) architecture was introduced by Kyunghyun Cho and colleagues in 2014. Like Long Short-Term Memory (LSTM) networks, GRUs are designed to address the limitations of standard RNNs in learning long-range dependencies within sequential data.

GRUs simplify the LSTM architecture by combining the input and forget gates into a single “update” gate, and by merging the cell state and hidden state. This results in a more compact and computationally efficient model, while still providing the capability to capture long-term dependencies.

The GRU has two main gates:

  1. Update gate: Determines how much of the previous hidden state should be retained and how much of the new input should be used to update the hidden state.
  2. Reset gate: Controls the degree to which the previous hidden state contributes to the candidate’s hidden state, allowing the GRU to capture short-term dependencies.

GRUs have been successfully applied to various sequence-related tasks, such as natural language processing, machine translation, speech recognition, and time-series prediction. In some cases, they offer similar performance to LSTMs but with faster training and inference times due to their simpler architecture. However, the choice between LSTMs and GRUs often depends on the specific problem and dataset, as the performance trade-offs can vary.

  • Abbreviation: GRU

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