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Found 4 papers, 2 papers with code
Upgrading VAE Training With Unlimited Data Plans Provided by Diffusion Models
Variational autoencoders (VAEs) are popular models for representation learning but their encoders are susceptible to overfitting (Cremer et al., 2018) because they are trained on a finite training set instead of the true (continuous) data distribution $p_{\mathrm{data}}(\mathbf{x})$.
The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatch
However, with this work, we aim to warn the community about an issue of the SVHN dataset as a benchmark for generative modeling tasks: we discover that the official split into training set and test set of the SVHN dataset are not drawn from the same distribution.
Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers
Annealed Importance Sampling (AIS) moves particles along a Markov chain from a tractable initial distribution to an intractable target distribution.
ProbNum: Probabilistic Numerics in Python
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference.
