CIF: III: Medium: MoDL+: Analytical Foundations for Deep Learning and Inference over Graphs

CIF：III：媒介：MoDL：深度学习和图推理的分析基础

基本信息

批准号：
2212318
负责人：
Jeffrey Calder
金额：
$ 119.98万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212318&HistoricalAwards=false
关键词：
CIF III Medium MoDL+Analytical

项目摘要

Deep learning, based on deep neural networks (DNNs), has demonstrated superior power in solving many difficult real-world problems, such as image classification, strategy-game playing, speech recognition, and medical image analysis, and is poised to revolutionize science and engineering, bringing broad benefits to society at large. Building on the success of DNNs, recent years have seen a flurry of research activities focused on developing graph neural networks (GNNs) in order to tackle important problems on graph-structured data. This award will address fundamental theoretical problems with deep GNNs, shedding light on their power and limitations and leading to new well-grounded GNN architectures. Guided by theory, the team of researchers will develop deep graph-learning algorithms for solving practical problems in 5G/NextG networks and power grids. The insights gained from this research will benefit diverse research domains, and aid in managing and securing physical and digital infrastructure. The award will also support undergraduate students, graduate students, and postdoctoral researchers from underrepresented minority groups in research and educational activities as well as organization of K-12 outreach programs.This award will advance a theory-guided and application-driven paradigm for tackling challenging fundamental research questions in deep graph learning, with a particular emphasis on applications to 5G/NextG wireless networks and power (micro)grid systems. The award will make connections between the theory of partial differential equations (PDEs) and deep graph-guided learning by establishing continuum limits for deep graph neural networks, utilizing PDE-guided deep graph neural networks, and using a novel Morse theory approach to understand the generalization power of GNNs. It will also advance innovative sensitivity-regularized deep-learning approaches, and provide an in-depth empirical study of the representation power of GNNs compared to standard DNNs, demystifying the role of graphs in deep learning. The project will help lay the needed theoretical foundation to guide the design of theory-guided deep graph learning algorithms to solve practical problems in 5G/NextG networks and power grids in a principled manner.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.

基于深度神经网络（DNNS）的深度学习在解决许多困难的现实问题方面表现出了卓越的力量，例如图像分类，策略游戏游戏，语音识别和医学图像分析，并有望彻底改变科学和工程，从而为整个社会带来广泛的好处。近年来，基于DNN的成功，一系列研究活动着重于开发图形神经网络（GNNS），以解决图形结构数据的重要问题。该奖项将通过深度GNN来解决基本的理论问题，阐明了它们的力量和局限性，并导致了新的GNN GNN架构。在理论的指导下，研究人员将开发深层的图形学习算法，以解决5G/NextG网络和电网中的实际问题。从这项研究中获得的见解将使各种研究领域受益，并有助于管理和确保物理和数字基础设施。该奖项还将支持来自代表性不足的少数群体的研究和教育活动的本科生，研究生和博士后研究人员，以及K-112宣传计划的组织。该奖项将提高一个理论和应用程序驱动的范式，以解决深度学习中的挑战性研究问题，并针对应用程序进行了挑战性的基本研究问题，并针对应用程序进行了挑战性的基本研究问题，并无需使用MICRORINDENTACTINACTINIDENTINS INDEACTIONS（MICRORINES），以及5G/NEXT GRORINGISS（5G/NEXT）（5G）。该奖项将通过建立深图神经网络的连续性限制，利用PDE引导的深度图神经网络，并使用新颖的摩尔斯理论方法来了解GNNS的普遍化能力，从而通过建立深图神经网络的连续性限制来建立连续限制，从而建立偏微分方程（PDE）的理论和深图指导学习之间的联系。它还将推进创新的敏感性调节性深度学习方法，并提供与标准DNN相比，对GNNS代表力的深入实证研究，从而揭示了图在深度学习中的作用。该项目将有助于奠定所需的理论基础，以指导理论引导的深度图学习算法的设计，以原则上的方式解决5G/NextG网络和电网中的实际问题。该奖项反映了NSF的法定任务，并被认为是通过该基金会的知识分子功能和广泛影响的评估来评估Criteria criteria criteria criteria criteria。