Methods › General › Regularization › GMVAE

Gaussian Mixture Variational Autoencoder

GMVAE

5 papers tagged archive 2025-07-28

Introduced by Aurora Cobo Aguilera et al. in Regularizing Transformers With Deep Probabilistic Layers

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

GMVAE, or Gaussian Mixture Variational Autoencoder, is a stochastic regularization layer for transformers. A GMVAE layer is trained using a 700-dimensional internal representation of the first MLP layer. For every output from the first MLP layer, the GMVAE layer first computes a latent low-dimensional representation sampling from the GMVAE posterior distribution to then provide at the output a reconstruction sampled from a generative model.

PaperSource

Papers archive 2025-07-28

5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Dimensionality Reduction2
Benchmarking1
Classification1
Decoder1
Game Design1
Metric Learning1
Protein Folding1

Usage over time archive 2025-07-28

Papers per year tagged with GMVAE: 2021 to 2025, peak 2 2 0 2021: 2 papers 2021 2022: 1 paper 2022 2023: 0 papers 2023 2024: 1 paper 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Regularization

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