A clear, math-first walkthrough of how VAEs learn to generate new data The post Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick appeared first on Towards Data Science .
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arXiv:2606. 25900v1 Announce Type: new Abstract: Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by producing a smooth and continuous latent space.
Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by producing a smooth and continuous latent space. Despite being introduced over a decade ago, the method continues to be widely adopted in both research and industry for diverse applications.
arXiv:2501. 09876v3 Announce Type: replace-cross Abstract: Generative modeling aims to generate new data samples that resemble a given dataset.
Introduction Heavy computation is a well-known problem in various ML algorithms today, especially when generative AI is applied to text, images, and other unstructured data. One of the principal approaches to mitigate this problem is to compress input data into a lower-dimensional representation while preserving the main context.
arXiv:2410. 10137v5 Announce Type: replace Abstract: We develop Riemannian approaches to variational autoencoders (VAEs) for PDE-type ambient data with regularizing geometric latent dynamics, which we refer to as VAE-DLM, or VAEs with dynamical latent manifolds.