Search Results for author: Oskar Kviman

Found 7 papers, 6 papers with code

Efficient Mixture Learning in Black-Box Variational Inference

1 code implementation11 Jun 2024 Alexandra Hotti, Oskar Kviman, Ricky Molén, Víctor Elvira, Jens Lagergren

However, currently scaling the number of mixture components can lead to a linear increase in the number of learnable parameters and a quadratic increase in inference time due to the evaluation of the evidence lower bound (ELBO).

Density Estimation Variational Inference

Improved Variational Bayesian Phylogenetic Inference using Mixtures

1 code implementation2 Oct 2023 Oskar Kviman, Ricky Molén, Jens Lagergren

We present VBPI-Mixtures, an algorithm designed to enhance the accuracy of phylogenetic posterior distributions, particularly for tree-topology and branch-length approximations.

Density Estimation Variational Inference

Statistical Distance Based Deterministic Offspring Selection in SMC Methods

no code implementations23 Dec 2022 Oskar Kviman, Hazal Koptagel, Harald Melin, Jens Lagergren

Over the years, sequential Monte Carlo (SMC) and, equivalently, particle filter (PF) theory has gained substantial attention from researchers.

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

1 code implementation30 Sep 2022 Oskar Kviman, Ricky Molén, Alexandra Hotti, Semih Kurt, Víctor Elvira, Jens Lagergren

In this work, we also demonstrate that increasing the number of mixture components improves the latent-representation capabilities of the VAE on both image and single-cell datasets.

Variational Inference

VaiPhy: a Variational Inference Based Algorithm for Phylogeny

1 code implementation1 Mar 2022 Hazal Koptagel, Oskar Kviman, Harald Melin, Negar Safinianaini, Jens Lagergren

The exponential size of the tree space is, unfortunately, a substantial obstacle for Bayesian phylogenetic inference using Markov chain Monte Carlo based methods since these rely on local operations.

Density Estimation Variational Inference

Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations

1 code implementation22 Feb 2022 Oskar Kviman, Harald Melin, Hazal Koptagel, Víctor Elvira, Jens Lagergren

In variational inference (VI), the marginal log-likelihood is estimated using the standard evidence lower bound (ELBO), or improved versions as the importance weighted ELBO (IWELBO).

Density Estimation Variational Inference

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