Which of the following is a type of recurrent neural network (RNN) variant?

Long Short-Term Memory (LSTM) is recognized as a significant variant of recurrent neural networks (RNNs). LSTMs are specifically designed to effectively capture long-term dependencies in sequential data, overcoming the limitations of traditional RNNs, which can struggle with remembering information across many time steps due to issues like vanishing gradients.

LSTMs achieve this through a more complex architecture that includes memory cells and various gates (input, output, and forget gates) that control the flow of information. This structure allows LSTMs to maintain relevant information for extended periods, making them especially useful in applications like natural language processing, speech recognition, and time series forecasting.

In contrast, the other options represent different types of neural network architectures that are not related to RNNs. Support Vector Machines are a type of algorithm used for classification and regression tasks but do not involve sequential data processing. Generative Adversarial Networks consist of two networks competing against each other to generate new data instances, which is also distinct from RNNs. Finally, Convolutional Neural Networks are primarily used for processing grid-like data such as images, focusing on spatial hierarchies rather than sequential dependencies.