<ul><li>Variational Autoencoders (VAEs) are widely used for probabilistic unsupervised learning with neural networks.</li><li>Recently, there has been a growing interest in discrete latent spaces, particularly for data modalities like text.</li><li>A tutorial has been provided on discrete variational autoencoders, focusing on VAEs with latent variables following a categorical distribution.</li><li>The tutorial covers the theoretical foundation, practical aspects, training guidelines, and includes an example implementation.</li></ul>

An Introduction to Discrete Variational Autoencoders

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