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Colloquium PR[AI]RIE

MCMC, Variational Inference, Invertible Flows… Bridging the gap?


Speaker: Éric Moulines, École Polytechnique


Variational Autoencoders (VAE) — generative models combining variational inference and autoencoding — have found widespread applications to learn latent representations for high-dimensional observations. However, most VAEs, relying on simple mean-field variational distributions, usually suffer from somewhat limited expressiveness, which results in a poor approximation of the conditional latent distribution and in particular mode dropping. In this work, we propose Metropolized VAE (MetVAE), a VAE approach based on a new class of variational distributions enriched with Markov Chain Monte Carlo. We develop a specific instance of MetVAE with Hamiltonian Monte Carlo and demonstrate clear improvements of the latent distribution approximations at the cost of a moderate increase of the computational cost. We consider application to probabilistic collaborative filtering models, and numerical experiments on classical benchmarks support the performance of MetVAE.