Recurrent Layer

Recurrent layers are fundamental components in neural networks designed to process sequential data by maintaining an internal state that evolves over time, enabling the modeling of long-range dependencies. Current research focuses on optimizing recurrent layer architectures, such as LSTMs and GRUs, for efficiency and performance in various applications, including large language models and time series prediction, exploring connectivity patterns, and addressing challenges like quantization and catastrophic forgetting in continual learning scenarios. These improvements are crucial for deploying complex models on resource-constrained devices and enhancing their capabilities in tasks requiring memory and temporal context, such as speech recognition, autonomous driving, and high-energy physics.