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Found 6 papers, 5 papers with code
Indirectly Parameterized Concrete Autoencoders
Feature selection is a crucial task in settings where data is high-dimensional or acquiring the full set of features is costly.
Improved Variational Bayesian Phylogenetic Inference using Mixtures
We present VBPI-Mixtures, an algorithm designed to enhance the accuracy of phylogenetic posterior distributions, particularly for tree-topology and branch-length approximations.
Statistical Distance Based Deterministic Offspring Selection in SMC Methods
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
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.
VaiPhy: a Variational Inference Based Algorithm for Phylogeny
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.
Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations
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).
