Which type of sequence model can effectively maintain relevant information over longer sequences?

Long short-term memory neural networks (LSTM) are specifically designed to address the limitations of traditional recurrent neural networks (RNNs) in learning from long sequences of data. LSTMs utilize a unique architecture that includes memory cells, which enable them to maintain information over extended periods. This is accomplished through mechanisms known as gates, which control the flow of information into, out of, and within the memory cells.

The design of LSTMs helps to combat the problem of vanishing gradients that often occurs in standard RNNs when modeling long-range dependencies. By effectively managing which information should be remembered or forgotten, LSTMs can capture and retain context and patterns over longer sequences, making them particularly suitable for tasks such as natural language processing, time series forecasting, and other sequence-related applications.

In contrast, recurrent neural networks, while also designed for sequential data, struggle with effectively maintaining information over long sequences. Convolutional neural networks (CNNs) are primarily designed for processing grid-like data such as images and do not have a built-in mechanism to handle sequential dependencies. Short-term memory networks, while a conceptual step toward capturing sequence data, do not possess the robust capabilities of LSTMs in managing and retaining information over longer time frames.