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Found 22 papers, 14 papers with code
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference
As an alternative, we propose SMC-Wake, a procedure for fitting an amortized variational approximation that uses likelihood-tempered sequential Monte Carlo samplers to estimate the gradient of the inclusive KL divergence.
Diffusion Models for Probabilistic Deconvolution of Galaxy Images
As an alternative, we propose a classifier-free conditional diffusion model for PSF deconvolution of galaxy images.
Amortized Variational Inference with Coverage Guarantees
We prove lower bounds on the predictive efficiency of the regions produced by CANVI and explore how the quality of a posterior approximation relates to the predictive efficiency of prediction regions based on that approximation.
Statistical Inference for Coadded Astronomical Images
Coadded astronomical images are created by stacking multiple single-exposure images.
Dynamic Survival Transformers for Causal Inference with Electronic Health Records
In medicine, researchers often seek to infer the effects of a given treatment on patients' outcomes.
Scalable Bayesian Inference for Detection and Deblending in Astronomical Images
We present a new probabilistic method for detecting, deblending, and cataloging astronomical sources called the Bayesian Light Source Separator (BLISS).
Normalizing Flows for Knockoff-free Controlled Feature Selection
We propose a new procedure called FlowSelect to perform controlled feature selection that does not suffer from either of these two problems.
Variational Inference for Deblending Crowded Starfields
In images collected by astronomical surveys, stars and galaxies often overlap visually.
An Empirical Comparison of GANs and Normalizing Flows for Density Estimation
Generative adversarial networks (GANs) and normalizing flows 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.
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.
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
We wish to compute the gradient of an expectation over a finite or countably infinite sample space having $K \leq \infty$ categories.
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.
Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets.
Cataloging the Visible Universe through Bayesian Inference at Petascale
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
Stochastic Cubic Regularization for Fast Nonconvex Optimization
This paper proposes a stochastic variant of a classic algorithm---the cubic-regularized Newton method [Nesterov and Polyak 2006].
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.
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
We introduce TrustVI, a fast second-order algorithm for black-box variational inference based on trust-region optimization and the reparameterization trick.
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results.
Celeste: Variational inference for a generative model of astronomical images
We present a new, fully generative model of optical telescope image sets, along with a variational procedure for inference.
