Learning to Bound: A Generative Cramér-Rao Bound-Reference-Cited by-同舟云学术

Learning to Bound: A Generative Cramér-Rao Bound

Published:2023 Issue: Volume:71 Page:1216-1231
ISSN:1053-587X
Container-title:IEEE Transactions on Signal Processing
language:
Short-container-title:IEEE Trans. Signal Process.

Author:

Habi Hai Victor¹^ORCID,Messer Hagit¹^ORCID,Bresler Yoram²^ORCID

Affiliation:

1. School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel

2. Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois at, Urbana-Champaign, Urbana, IL, USA

Funder

ARO

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/78/10040758/10066847.pdf?arnumber=10066847

Reference48 articles.

1. Normalizing Flows: An Introduction and Review of Current Methods

2. Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

3. Parallel wavenet: Fast high-fidelity speech synthesis;oord;Proc Int Conf Mach Learn,0

4. Cubic-spline flows;durkan;ICML workshop on Invertible Neural Networks and Normalizing Flows,2019

5. Performance Bounds for Parameter Estimation under Misspecified Models: Fundamental Findings and Applications

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