Gated Recurrent Unit (GRU) Paper

The Gated Recurrent Unit (GRU) paper introduces a type of neural network designed for processing sequential data, like language or time series, more efficiently than older models. GRUs use special mechanisms called "gates" to regulate information flow, allowing the network to retain relevant details and forget less useful ones over time. This helps it capture long-range dependencies in data without becoming overly complex or slow to train. Overall, GRUs improve the ability of machines to understand sequences, making tasks like language translation or speech recognition more effective and faster.