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Found 10 papers, 7 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.
Learning Stationary Markov Processes with Contrastive Adjustment
We introduce a new optimization algorithm, termed contrastive adjustment, for learning Markov transition kernels whose stationary distribution matches the data distribution.
Ranked #120 on
Image Generation
on CIFAR-10
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).
The Klarna Product Page Dataset: Web Element Nomination with Graph Neural Networks and Large Language Models
To address this, we introduce the Klarna Product Page Dataset, a comprehensive and diverse collection of webpages that surpasses existing datasets in richness and variety.
Viewpoint and Topic Modeling of Current Events
There are multiple sides to every story, and while statistical topic models have been highly successful at topically summarizing the stories in corpora of text documents, they do not explicitly address the issue of learning the different sides, the viewpoints, expressed in the documents.
A Global Structural EM Algorithm for a Model of Cancer Progression
Cancer has complex patterns of progression that include converging as well as diverging progressional pathways.
