Variational autoencoders (VAEs) are a class of generative models that use neural networks to learn efficient representations of data in an unsupervised manner. They work by encoding input data into a latent space and then decoding it back to reconstruct the original data, while incorporating a probabilistic framework that allows them to model uncertainty and generate new samples from learned distributions.
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