93 citations · 202 across the 9 of their papers we have counts for
6 papers · 1 filter
AutoEval Done Right: Using Synthetic Data for Model Evaluation
Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef +2
The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming. AI-labeled synthetic data can be used to decrease the number of h…
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements
Romain Lopez, Achille Nazaret, Maxime Langevin +4
Spatial studies of transcriptome provide biologists with gene expression maps of heterogeneous and complex tissues. However, most experimental protocols for spatial transcriptomics…
A Deep Generative Model for Semi-Supervised Classification with Noisy Labels
Maxime Langevin, Edouard Mehlman, Jeffrey Regier +3
Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among classes. We propose a new semi-supervised deep generative model that explicitly models n…
Information Constraints on Auto-Encoding Variational Bayes
Romain Lopez, Jeffrey Regier, Michael I. Jordan +1
Parameterizing the approximate posterior of a generative model with neural networks has become a common theme in recent machine learning research. While providing appealing flexibi…
A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes
Romain Lopez, Jeffrey Regier, Michael Cole +2
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent…
A deep generative model for gene expression profiles from single-cell RNA sequencing
Romain Lopez, Jeffrey Regier, Michael Cole +2
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent…