NSF-BSF: Collaborative Research: CIF: Small: Neural Estimation of Statistical Divergences: Theoretical Foundations and Applications to Communication Systems

NSF-BSF：协作研究：CIF：小型：统计差异的神经估计：通信系统的理论基础和应用

• 批准号: 2308446
• 负责人: Ziv Goldfeld
• 金额: $ 20万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308446&HistoricalAwards=false
• 关键词: NSF; BSF; Collaborative; Research; CIF

An abundance of data and recent advances in computation have dramatically increased the reliability and performance of information processing systems. In many applications, these systems provide approximate answers to statistical questions about the distributions that generate the data. Estimators based on neural networks have become the method of choice when dealing with large and complicated datasets. Their popularity is due to their excellent performance in practice and their computational efficiency. But knowledge of the reasons behind their outstanding performance remains quite limited. The goal of this project is to improve the understanding of why neural estimation works and to provide formal performance guarantees that help guide practical applications in fields such as wireless communications. In addition to technological developments, the project features graduate student mentoring, undergraduate inclusion, outreach to high school via a summer camp, international collaboration, and the development of tutorial videos.This project models questions about data using statistical divergences that measure the discrepancy between probability distributions. While there are many approaches to estimating statistical divergences from data, neural estimators have now become the method of choice when dealing with large, high-dimensional datasets. The project will develop a comprehensive statistical and computational theory of neural estimation and apply it to novel applications to communications. It has two main thrusts. The first targets a non-asymptotic neural estimation theory that accounts for all known sources of error: functional approximation, empirical estimation, and optimization. The second will devise a flexible, efficiently computable, and provably accurate methodology for neural capacity estimation and data-driven polar coding.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

丰富的数据和计算的最新进展极大地提高了信息处理系统的可靠性和性能。在许多应用中，这些系统为有关生成数据的分布的统计问题提供近似答案。基于神经网络的估计器已成为处理大型复杂数据集时的首选方法。它们的受欢迎是由于它们在实践中的出色表现和计算效率。但对其出色表现背后原因的了解仍然相当有限。该项目的目标是加深对神经估计工作原理的理解，并提供正式的性能保证，帮助指导无线通信等领域的实际应用。除了技术发展之外，该项目还包括研究生指导、本科生包容、通过夏令营向高中进行推广、国际合作以及教程视频的开发。该项目使用统计差异来衡量概率之间的差异，对有关数据的问题进行建模分布。虽然有许多方法可以估计数据的统计差异，但神经估计器现在已成为处理大型高维数据集时的首选方法。该项目将开发神经估计的综合统计和计算理论，并将其应用于通信的新应用。它有两个主要推力。第一个目标是非渐近神经估计理论，该理论解释了所有已知的误差源：函数逼近、经验估计和优化。第二个奖项将设计一种灵活、可高效计算且可证明准确的方法，用于神经容量估计和数据驱动的极坐标编码。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。 。