Search Results for author:
Found 11 papers, 3 papers with code
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
The proposed approach enables the estimation of the probability that two sets of samples are drawn from the same distribution, providing a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models trained on the same dataset.
Improving Gradient-guided Nested Sampling for Posterior Inference
We present a performant, general-purpose gradient-guided nested sampling algorithm, ${\tt GGNS}$, combining the state of the art in differentiable programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization.
Bayesian Imaging for Radio Interferometry with Score-Based Priors
The inverse imaging task in radio interferometry is a key limiting factor to retrieving Bayesian uncertainties in radio astronomy in a computationally effective manner.
Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors
Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms.
Lie Point Symmetry and Physics Informed Networks
Symmetries have been leveraged to improve the generalization of neural networks through different mechanisms from data augmentation to equivariant architectures.
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
We demonstrate the method on a variety of synthetic examples, and show that TARP can be used to test the results of posterior inference analyses in high-dimensional spaces.
Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines
In this work, we use a neural network based on the Recurrent Inference Machine (RIM) to simultaneously reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps.
Posterior samples of source galaxies in strong gravitational lenses with score-based priors
Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge, in part due to the difficulties of accurately quantifying the priors.
deep21: a Deep Learning Method for 21cm Foreground Removal
We seek to remove foreground contaminants from 21cm intensity mapping observations.
HInet: Generating neutral hydrogen from dark matter with neural networks
Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) over very large cosmological volumes.
Cosmology and Nongalactic Astrophysics
Bayesian Neural Networks
In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models.
