Discovering and decoding latent mean-field structure with variational autoencoders
Researchers have made breakthroughs in using variational autoencoders (VAEs) for generative learning, particularly in capturing correlations in many-body systems and multimodal data.
A study published on arXiv[1] demonstrates that VAEs can faithfully reconstruct the joint probability distribution of a many-body system by comparing the rate of the latent channel to the bipartite mutual information of the data. The researchers found that a successful VAE reconstruction is direct evidence for a latent mean-field theory, and the microscopic parameters of the theory can be read off the trained decoder. They validated their conclusions on a hierarchy of solvable models with scalar, vector, and tensor order parameters. Meanwhile, another study on arXiv[2] introduced Hellinger Multimodal Variational Autoencoders (HELVAE), which avoids sub-sampling and yields an efficient yet effective model for weakly supervised generative learning with multiple modalities. HELVAE learns more expressive latent representations as additional modalities are observed and achieves better trade-offs between generative coherence and quality, outperforming state-of-the-art multimodal VAE models. Both studies were submitted in 2026[1][2].
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Background sources we checked (4)
- arxiv.org ↗ Generative models are increasingly used to capture correlations in many-body systems, but the representations they learn remain largely opaque to physical interpretation. Here, we establish an intuitive criterion that quantifies the capacity of a variational autoencoder (VAE) to …
- en.wikipedia.org ↗ In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling in 2013. It is part of the families of probabilistic graphical models and variational Bayesian methods. In addition to being seen as …
- en.wikipedia.org ↗ Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, software code (vibe coding) or other forms of data. These models learn the underlying patterns and structures of their tra…
- en.wikipedia.org ↗ In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of two major components: the forward diffusion process, and the reverse sampling p…