Search Results for author: Davis Gilton

Found 7 papers, 3 papers with code

Masked LARk: Masked Learning, Aggregation and Reporting worKflow

1 code implementation27 Oct 2021 Joseph J. Pfeiffer III, Denis Charles, Davis Gilton, Young Hun Jung, Mehul Parsana, Erik Anderson

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.

Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder

no code implementations8 Mar 2021 Takuya Kurihana, Elisabeth Moyer, Rebecca Willett, Davis Gilton, Ian Foster

Advanced satellite-born remote sensing instruments produce high-resolution multi-spectral data for much of the globe at a daily cadence.

Deep Equilibrium Architectures for Inverse Problems in Imaging

1 code implementation16 Feb 2021 Davis Gilton, Gregory Ongie, Rebecca Willett

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.

Model Adaptation for Inverse Problems in Imaging

no code implementations30 Nov 2020 Davis Gilton, Gregory Ongie, Rebecca Willett

Deep neural networks have been applied successfully to a wide variety of inverse problems arising in computational imaging.

Deblurring Image Reconstruction +1

Detection and Description of Change in Visual Streams

no code implementations27 Mar 2020 Davis Gilton, Ruotian Luo, Rebecca Willett, Greg Shakhnarovich

This paper presents a framework for the analysis of changes in visual streams: ordered sequences of images, possibly separated by significant time gaps.

Representation Learning

Learning to Solve Linear Inverse Problems in Imaging with Neumann Networks

no code implementations NeurIPS Workshop Deep_Invers 2019 Greg Ongie, Davis Gilton, Rebecca Willett

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.

Neumann Networks for Inverse Problems in Imaging

2 code implementations13 Jan 2019 Davis Gilton, Greg Ongie, Rebecca Willett

We present an end-to-end, data-driven method of solving inverse problems inspired by the Neumann series, which we call a Neumann network.


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