Ricky Molén — accepted AI-conference papers

2024

Efficient Mixture Learning in Black-Box Variational Inference

Alexandra Hotti, Oskar Kviman, Ricky Molén, Víctor Elvira, Jens Lagergren

ICML 2024poster

Mixture variational distributions in black box variational inference (BBVI) have demonstrated impressive results in challenging density estimation tasks. However, currently scaling the number of mixture components can lead to a linear increase in the number of learnable parameters and a quadratic in…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗

2023

Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders

Oskar Kviman, Ricky Molén, Alexandra Hotti, Semih Kurt, Víctor Elvira, Jens Lagergren

ICML 2023poster

In this paper, we show how the mixture components cooperate when they jointly adapt to maximize the ELBO. We build upon recent advances in the multiple and adaptive importance sampling literature. We then model the mixture components using separate encoder networks and show empirically that the ELBO…

Cited by 12 Source ↗PDF ↗code ↗Scholar ↗