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Found 7 papers, 5 papers with code
AutoEval Done Right: Using Synthetic Data for Model Evaluation
The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming.
Decision-Making with Auto-Encoding Variational Bayes
To make decisions based on a model fit with auto-encoding variational Bayes (AEVB), practitioners often let the variational distribution serve as a surrogate for the posterior distribution.
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements
Building upon domain adaptation work, we propose gimVI, a deep generative model for the integration of spatial transcriptomic data and scRNA-seq data that can be used to impute missing genes.
A Deep Generative Model for Semi-Supervised Classification with Noisy Labels
Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among classes.
Information Constraints on Auto-Encoding Variational Bayes
We show how to apply this method to a range of problems, including the problems of learning invariant representations and the learning of interpretable representations.
A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing.
A deep generative model for gene expression profiles from single-cell RNA sequencing
We also extend our framework to account for batch effects and other confounding factors, and propose a Bayesian hypothesis test for differential expression that outperforms DESeq2.
