Search Results for author:
Found 7 papers, 3 papers with code
Neumann Networks for Inverse Problems in Imaging
We present an end-to-end, data-driven method of solving inverse problems inspired by the Neumann series, which we call a Neumann network.
Learning to Solve Linear Inverse Problems in Imaging with Neumann Networks
Recent advances have illustrated that it is often possible to learn to solve linear inverse problems in imaging using training data that can outperform more traditional regularized least squares solutions.
Detection and Description of Change in Visual Streams
This paper presents a framework for the analysis of changes in visual streams: ordered sequences of images, possibly separated by significant time gaps.
Model Adaptation for Inverse Problems in Imaging
Deep neural networks have been applied successfully to a wide variety of inverse problems arising in computational imaging.
Deep Equilibrium Architectures for Inverse Problems in Imaging
Recent efforts on solving inverse problems in imaging via deep neural networks use architectures inspired by a fixed number of iterations of an optimization method.
Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder
Advanced satellite-born remote sensing instruments produce high-resolution multi-spectral data for much of the globe at a daily cadence.
Masked LARk: Masked Learning, Aggregation and Reporting worKflow
We introduce a secure multi-party compute (MPC) protocol that utilizes "helper" parties to train models, so that once data leaves the browser, no downstream system can individually construct a complete picture of the user activity.
