Search Results for author: Yashar Hezaveh

Found 8 papers, 3 papers with code

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

no code implementations6 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.

Dimensionality Reduction

Improving Gradient-guided Nested Sampling for Posterior Inference

1 code implementation6 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.

Clustering

Bayesian Imaging for Radio Interferometry with Score-Based Priors

no code implementations29 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.

Astronomy Radio Interferometry

On Diffusion Modeling for Anomaly Detection

1 code implementation29 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).

Denoising Semi-supervised Anomaly Detection +1

Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

1 code implementation6 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.

Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines

no code implementations10 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.

Posterior samples of source galaxies in strong gravitational lenses with score-based priors

no code implementations7 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.

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