Personal tools
Creation Date: Jul 07, 2019
Published In: Jul 2019
Paper Type: Conference Paper
Book Title: Proceedings of the 2019 IEEE International Symposium on Information Theory
Address: Paris, France
Abstract:Deep generative priors are a powerful tool for reconstruction problems with complex data such as images and text. Inverse problems using such models require solving an inference problem of estimating the input and hidden units of the multi-layer network from its output. Maximum a priori (MAP) estimation is a widely-used inference method as it is straightforward to implement, and has been successful in practice. However, rigorous analysis of MAP inference in multi-layer networks is difficult. This work considers a recently-developed method, multilayer vector approximate message passing (ML-VAMP), to study MAP inference in deep networks. It is shown that the mean squared error of the ML-VAMP estimate can be exactly and rigorously characterized in a certain high-dimensional random limit. The proposed method thus provides a tractable method for MAP inference with exact performance guarantees.
IEEE Explore link: https://ieeexplore.ieee.org/document/8849316
Award(s) Received: |
Home | Contact | Accessibility | Nondiscrimination Policy | Privacy & Opting Out of Cookies
© Copyright 2019 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.