Standard LSTM

Standard Long Short-Term Memory (LSTM) networks are recurrent neural networks designed to process sequential data by addressing the vanishing gradient problem inherent in simpler recurrent architectures. Current research focuses on improving LSTM efficiency and performance through techniques like vectorization, incorporating attention mechanisms, and integrating LSTMs with other architectures such as transformers and autoencoders for tasks ranging from activity recognition and speech intelligibility classification to stock price prediction and novelty detection in system call traces. These advancements enhance the applicability of LSTMs across diverse fields, improving accuracy and interpretability while addressing challenges like handling long sequences and incorporating contextual information.