Search Results for author: Warren R. Morningstar

Found 5 papers, 0 papers with code

PAC$^m$-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime

no code implementations19 Oct 2020 Warren R. Morningstar, Alexander A. Alemi, Joshua V. Dillon

To formally understand the consequences of Bayesian misspecification, this work examines the relationship between posterior predictive risk and its sensitivity to correct model assumptions, i. e., choice of likelihood and prior.

Density of States Estimation for Out-of-Distribution Detection

no code implementations16 Jun 2020 Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon

Drawing on the statistical physics notion of ``density of states,'' the DoSE decision rule avoids direct comparison of model probabilities, and instead utilizes the ``probability of the model probability,'' or indeed the frequency of any reasonable statistic.

Out-of-Distribution Detection

Automatic Differentiation Variational Inference with Mixtures

no code implementations3 Mar 2020 Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham, Andrew Gallagher, Joshua V. Dillon

Automatic Differentiation Variational Inference (ADVI) is a useful tool for efficiently learning probabilistic models in machine learning.

Variational Inference

Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines

no code implementations5 Jan 2019 Warren R. Morningstar, Laurence Perreault Levasseur, Yashar D. Hezaveh, Roger Blandford, Phil Marshall, Patrick Putzky, Thomas D. Rueter, Risa Wechsler, Max Welling

We present a machine learning method for the reconstruction of the undistorted images of background sources in strongly lensed systems.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies

Analyzing interferometric observations of strong gravitational lenses with recurrent and convolutional neural networks

no code implementations31 Jul 2018 Warren R. Morningstar, Yashar D. Hezaveh, Laurence Perreault Levasseur, Roger D. Blandford, Philip J. Marshall, Patrick Putzky, Risa H. Wechsler

We use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to estimate the parameters of strong gravitational lenses from interferometric observations.

Instrumentation and Methods for Astrophysics

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