18 citations · 25 across the 9 of their papers we have counts for
9 papers
Strong Lensing Cosmology with Population-level Calibrated Neural Ratio Estimation
Sreevani Jarugula, Brian D. Nord, Aleksandra Ćiprijanović +1
Strong gravitational lensing contains key information about cosmic acceleration. Modern and next-generation galaxy imaging surveys are expected to provide high-quality data on $\ma…
On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning
Shubhendu Trivedi, Brian D. Nord
Quantifying uncertainties for machine learning (ML) models is a foundational challenge in modern data analysis. This challenge is compounded by at least two key aspects of the fiel…
SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks
Sneh Pandya, Purvik Patel, Brian D. Nord +2
Modern neural networks (NNs) often do not generalize well in the presence of a "covariate shift"; that is, in situations where the training and test data distributions differ, but…
Deep inference of simulated strong lenses in ground-based surveys
Jason Poh, Ashwin Samudre, Aleksandra Ćiprijanović +3
The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dar…
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…
Generalised Fisher Matrices
A. F. Heavens, M. Seikel, B. D. Nord +4
The Fisher Information Matrix formalism is extended to cases where the data is divided into two parts (X,Y), where the expectation value of Y depends on X according to some theoret…