Variational autoencoders (VAEs) are a type of deep learning model that combines neural networks with probabilistic graphical models to learn efficient representations of data in a lower-dimensional latent space. They are particularly useful for dimensionality reduction, as they can capture complex data distributions while allowing for the generation of new data points that resemble the original dataset. VAEs leverage variational inference to approximate the posterior distribution of the latent variables, enabling them to encode and reconstruct data in a way that is both informative and generative.
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