I'd like to chime in that Variational Autoencoder's theoretical framework is also quite useful in unsupervised learning tasks. It bridges variational inference

I'd like to chime in that Variational Autoencoder's theoretical framework is also quite useful in unsupervised learning tasks. It bridges variational inference methods (a powerful classical ML technique) with neural networks, thus allow very efficient representation, generalization and disentanglement.