Recurrent Network

Recurrent networks are neural network architectures designed to process sequential data by maintaining an internal state that evolves over time, enabling them to capture temporal dependencies. Current research focuses on improving their efficiency and scalability, particularly through novel architectures like state-space models and modifications to classic RNNs (LSTMs, GRUs) that enable parallel training. This renewed interest stems from limitations in transformer models for long sequences and a desire for more biologically plausible learning algorithms, leading to advancements in areas like online learning and applications in diverse fields such as recommender systems, medical image registration, and robotics.