RNN Architecture

Recurrent Neural Networks (RNNs) are neural network architectures designed to process sequential data by incorporating feedback connections, enabling them to maintain a "memory" of past inputs. Current research focuses on improving RNN performance and interpretability through advancements in model architectures like Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and the integration of attention mechanisms, as well as exploring alternative approaches such as Transformers and Fast Weight Programmers. These efforts aim to address challenges such as vanishing/exploding gradients and enhance the applicability of RNNs in diverse fields, including time series forecasting, speech recognition, and control systems for robotics. The resulting improvements in accuracy and explainability are crucial for deploying RNNs in high-stakes applications requiring reliable and understandable predictions.