What does LSTM stand for in the context of RNNs?

LSTM stands for Long Short-Term Memory unit, which is a specialized type of recurrent neural network (RNN) architecture designed to address challenges related to training traditional RNNs. The LSTM architecture is particularly effective in capturing long-range dependencies in sequential data. This is achieved through the incorporation of memory cells that can maintain information over extended time intervals, as well as gating mechanisms that regulate the flow of information into, out of, and within the memory cells.

These features make LSTMs well-suited for tasks such as language modeling, speech recognition, and time series prediction, where context and sequential information from previous inputs are essential for making accurate predictions. By contrast, other terms listed in the options do not correspond to established concepts in the study of RNNs and would not accurately describe the function and structure of LSTMs.