no code implementations • 6 Feb 2024 • Pablo Lemos, Sammy Sharief, Nikolay Malkin, Laurence Perreault-Levasseur, Yashar Hezaveh
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.
1 code implementation • 6 Dec 2023 • Pablo Lemos, Nikolay Malkin, Will Handley, Yoshua Bengio, Yashar Hezaveh, Laurence Perreault-Levasseur
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.
no code implementations • 29 Nov 2023 • Noe Dia, M. J. Yantovski-Barth, Alexandre Adam, Micah Bowles, Pablo Lemos, Anna M. M. Scaife, Yashar Hezaveh, Laurence Perreault-Levasseur
The inverse imaging task in radio interferometry is a key limiting factor to retrieving Bayesian uncertainties in radio astronomy in a computationally effective manner.
no code implementations • 29 Nov 2023 • Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreault-Levasseur
Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms.
1 code implementation • 29 May 2023 • Victor Livernoche, Vineet Jain, Yashar Hezaveh, Siamak Ravanbakhsh
By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE).
1 code implementation • 6 Feb 2023 • Pablo Lemos, Adam Coogan, Yashar Hezaveh, Laurence Perreault-Levasseur
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.
no code implementations • 10 Jan 2023 • Alexandre Adam, Laurence Perreault-Levasseur, Yashar Hezaveh, Max Welling
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.
no code implementations • 7 Nov 2022 • Alexandre Adam, Adam Coogan, Nikolay Malkin, Ronan Legin, Laurence Perreault-Levasseur, Yashar Hezaveh, Yoshua Bengio
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.