Long Short-Term Memory

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network architecture designed to handle the vanishing gradient problem, enabling them to learn long-term dependencies in sequential data. This makes them particularly effective for tasks like music generation where understanding context over extended periods is crucial. Hochreiter and Schmidhuber's seminal paper introduces the LSTM architecture and demonstrates its capabilities in learning and remembering information over long sequences, which is fundamental to AI-driven music composition tools like Amadeus AI.