To circumvent the need to enforce the swap property, FlowSelect uses a novel MCMC-based procedure to directly compute p-values for each feature.
Normalizing flows and generative adversarial networks (GANs) are both approaches to density estimation that use deep neural networks to transform samples from an uninformative prior distribution to an approximation of the data distribution.
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.
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.
We wish to compute the gradient of an expectation over a finite or countably infinite sample space having $K \leq \infty$ categories.
Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among classes.
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.
We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets.
1 code implementation • 31 Jan 2018 • Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin Thomas, Prabhat
We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia.
Distributed, Parallel, and Cluster Computing Instrumentation and Methods for Astrophysics 85A35, 68W10, 62P35 J.2; D.1.3; G.3; I.2; D.2
This paper proposes a stochastic variant of a classic algorithm---the cubic-regularized Newton method [Nesterov and Polyak 2006].
We propose a probabilistic model for interpreting gene expression levels that are observed through 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.
We introduce TrustVI, a fast second-order algorithm for black-box variational inference based on trust-region optimization and the reparameterization trick.
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results.
We present a new, fully generative model of optical telescope image sets, along with a variational procedure for inference.