5 papers
Generative Diffusion Priors for 3D Mapping of the Dark Universe
Brandon Zhao, Diana Scognamiglio, Olivier Doré +1
Reconstructing the three-dimensional distribution of dark matter from weak-lensing observations is a central but highly ill-posed inverse problem in cosmology. Unlike standard 3D r…
Fourier Feature Pyramids for Physics-Informed Neural Networks
Brandon Zhao, Yixuan Wang, Jonathan T. Barron +3
We present an improved neural field architecture for solving partial differential equations (PDEs). Current physics-informed neural networks (PINNs) provide a flexible framework fo…
Optimizing Diffusion Priors in Image Reconstruction from a Single Observation
Frederic Wang, Katherine L. Bouman
While diffusion priors generate high-quality posterior samples across many inverse problems, they are often trained on limited training sets or purely simulated data, thus inheriti…
Sample-efficient evidence estimation of score based priors for model selection
Frederic Wang, Katherine L. Bouman
The choice of prior is central to solving ill-posed imaging inverse problems, making it essential to select one consistent with the measurements to avoid severe bias. In Bayesi…
Multi-resolution Data Fusion for Super-Resolution Electron Microscopy
Suhas Sreehari, S. V. Venkatakrishnan, Katherine L. Bouman +3
Perhaps surprisingly, the total electron microscopy (EM) data collected to date is less than a cubic millimeter. Consequently, there is an enormous demand in the materials and biol…