Search Results for author:
Found 8 papers, 5 papers with code
Joint Cryo-ET Alignment and Reconstruction with Neural Deformation Fields
We show that our approach enables the recovery of high-frequency details that are destroyed without accounting for deformations.
Karyotype AI for Precision Oncology
These individual chromosomes were used to train and assess deep learning models for classifying the 24 human chromosomes and identifying chromosomal aberrations.
Differentiable Uncalibrated Imaging
We propose a differentiable imaging framework to address uncertainty in measurement coordinates such as sensor locations and projection angles.
Conditional Injective Flows for Bayesian Imaging
Most deep learning models for computational imaging regress a single reconstructed image.
Trumpets: Injective Flows for Inference and Inverse Problems
We propose injective generative models called Trumpets that generalize invertible normalizing flows.
Globally Injective ReLU Networks
Injectivity plays an important role in generative models where it enables inference; in inverse problems and compressed sensing with generative priors it is a precursor to well posedness.
Learning the geometry of wave-based imaging
We propose a general physics-based deep learning architecture for wave-based imaging problems.
Random mesh projectors for inverse problems
We show that in this case the common approach to directly learn the mapping from the measured data to the reconstruction becomes unstable.
