18 citations · 25 across the 9 of their papers we have counts for
4 papers · 1 filter
DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods
Rebecca Nevin, Aleksandra Ćiprijanović, Brian D. Nord
Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physic…
Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification
Shrihan Agarwal, Aleksandra Ćiprijanović, Brian D. Nord
Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising appr…
Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens Analysis
Paxson Swierc, Marcos Tamargo-Arizmendi, Aleksandra Ćiprijanović +1
Modeling strong gravitational lenses is prohibitively expensive for modern and next-generation cosmic survey data. Neural posterior estimation (NPE), a simulation-based inference (…
Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks
Ashwin Samudre, Mircea Petrache, Brian D. Nord +1
There has been much recent interest in designing neural networks (NNs) with relaxed equivariance, which interpolate between exact equivariance and full flexibility for consistent p…