Recurrent Neural Network Architecture

Recurrent neural networks (RNNs) are a class of neural networks designed to process sequential data by maintaining an internal state that is updated over time. Current research focuses on improving RNN architectures, such as state space models (SSMs) and variations of LSTMs and GRUs, to enhance their ability to handle long sequences and complex temporal dependencies, often comparing their performance against Transformers. This work is driven by the need for efficient and interpretable models for various applications, including natural language processing, time-series forecasting, and control systems, where RNNs offer advantages in terms of computational efficiency and the ability to model causality. The development of novel RNN architectures and training techniques is leading to improved performance and a deeper understanding of their representational capabilities.