246 citations · 277 across the 5 of their papers we have counts for
10 papers
VIB is Half Bayes
Alexander A Alemi, Warren R Morningstar, Ben Poole +2
In discriminative settings such as regression and classification there are two random variables at play, the inputs X and the targets Y. Here, we demonstrate that the Variational I…
Hunting for Dark Matter Subhalos in Strong Gravitational Lensing with Neural Networks
Joshua Yao-Yu Lin, Hang Yu, Warren Morningstar +2
Dark matter substructures are interesting since they can reveal the properties of dark matter. Collisionless N-body simulations of cold dark matter show more substructures compared…
Density of States Estimation for Out-of-Distribution Detection
Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher +3
Perhaps surprisingly, recent studies have shown probabilistic model likelihoods have poor specificity for out-of-distribution (OOD) detection and often assign higher likelihoods to…
Automatic Differentiation Variational Inference with Mixtures
Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham +2
Automatic Differentiation Variational Inference (ADVI) is a useful tool for efficiently learning probabilistic models in machine learning. Generally approximate posteriors learned…
Source structure and molecular gas properties from high-resolution CO imaging of SPT-selected dusty star-forming galaxies
Chenxing Dong, Justin S. Spilker, Anthony H. Gonzalez +17
We present Atacama Large Millimeter/submillimeter Array (ALMA) observations of high-J CO lines (, 7, 8) and associated dust continuum towards five strongly lensed,…
Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines
Warren R. Morningstar, Laurence Perreault Levasseur, Yashar D. Hezaveh +6
We present a machine learning method for the reconstruction of the undistorted images of background sources in strongly lensed systems. This method treats the source as a pixelated…