Bidirectional Long Short Term Memory

Bidirectional Long Short-Term Memory (BiLSTM) networks are recurrent neural networks designed to process sequential data by considering both past and future context, improving upon the limitations of unidirectional approaches. Current research focuses on applying BiLSTMs, often in conjunction with other architectures like Convolutional Neural Networks (CNNs) and Transformers, to diverse tasks such as time series prediction (e.g., cryptocurrency prices, energy load forecasting), natural language processing (e.g., authorship attribution, sentiment analysis, question answering), and signal processing (e.g., speech separation, fault detection). This versatility makes BiLSTMs a significant tool across numerous fields, enabling improved accuracy and efficiency in various applications while also contributing to the development of more interpretable models.