2 code implementations • 7 Sep 2017 • Romain Lopez, Jeffrey Regier, Michael Cole, Michael Jordan, Nir Yosef
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.
2 code implementations • NeurIPS 2020 • Romain Lopez, Pierre Boyeau, Nir Yosef, Michael. I. Jordan, Jeffrey Regier
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.
2 code implementations • 6 May 2019 • Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael. I. Jordan, Nir Yosef
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.
1 code implementation • 16 Sep 2018 • Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael. I. Jordan, Nir Yosef
Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among classes.
1 code implementation • 9 Mar 2024 • Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra Malik, Michael I. Jordan
The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming.
no code implementations • NeurIPS 2018 • Romain Lopez, Jeffrey Regier, Michael. I. Jordan, Nir Yosef
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.
no code implementations • 13 Oct 2017 • Romain Lopez, Jeffrey Regier, Michael Cole, Michael Jordan, Nir Yosef
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing.