no code implementations • 2 Dec 2023 • Renan A. Rojas-Gomez, Karan Singhal, Ali Etemad, Alex Bijamov, Warren R. Morningstar, Philip Andrew Mansfield
Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images.
no code implementations • 19 Oct 2020 • Warren R. Morningstar, Alexander A. Alemi, Joshua V. Dillon
The Bayesian posterior minimizes the "inferential risk" which itself bounds the "predictive risk".
no code implementations • 16 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 Out of Distribution (OOD) Detection +1
no code implementations • 3 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.
no code implementations • 5 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
no code implementations • 31 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