132 citations · 134 across the 6 of their papers we have counts for
6 papers
Inverse problems with diffusion models: MAP estimation via mode-seeking loss
Sai Bharath Chandra Gutha, Ricardo Vinuesa, Hossein Azizpour
A pre-trained unconditional diffusion model, combined with posterior sampling or maximum a posteriori (MAP) estimation techniques, can solve arbitrary inverse problems without task…
Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments
Abhijeet Vishwasrao, Sai Bharath Chandra Gutha, Andres Cremades +8
Rapid urbanization demands efficient monitoring of turbulent wind and pollutant dispersion, yet existing reconstruction and sensor placement strategies fail under realistic sparsit…
Revisiting Score Function Estimators for -Subset Sampling
Klas Wijk, Ricardo Vinuesa, Hossein Azizpour
Are score function estimators an underestimated approach to learning with -subset sampling? Sampling -subsets is a fundamental operation in many machine learning tasks that i…
Inverse Problems with Diffusion Models: A MAP Estimation Perspective
Sai Bharath Chandra Gutha, Ricardo Vinuesa, Hossein Azizpour
Inverse problems have many applications in science and engineering. In Computer vision, several image restoration tasks such as inpainting, deblurring, and super-resolution can be…
Indirectly Parameterized Concrete Autoencoders
Alfred Nilsson, Klas Wijk, Sai bharath chandra Gutha +7
Feature selection is a crucial task in settings where data is high-dimensional or acquiring the full set of features is costly. Recent developments in neural network-based embedded…
From coarse wall measurements to turbulent velocity fields through deep learning
Alejandro Güemes, Stefano Discetti, Andrea Ianiro +3
This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse…