HDR TRIPODS: FINPenn: Center for the Foundations of Information Processing at the University of Pennsylvania

HDR TRIPODS：FINPenn：宾夕法尼亚大学信息处理基础中心

• 批准号: 1934960
• 负责人: Alejandro Ribeiro
• 金额: $ 150万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934960&HistoricalAwards=false
• 关键词: HDR; TRIPODS; FINPenn; Center; Foundations

Recent advances in artificial intelligence have led to significant progress in our ability to extract information from images and time sequences. Maintaining this rate of progress hinges upon attaining equally significant results in the processing of more complex signals such as those that are acquired by autonomous systems and networks of connected devices, or those that arise in the study of complex biological and social systems. This award establishes FINPenn, the Center for the Foundations of Information Processing at the University of Pennsylvania. The focus of the center is to establish fundamental theory to enable the study of data beyond time and images. The center's premise is that humans' rich intuitive understanding of space and time may not necessarily be applicable to the processing of complex signals. Therefore, matching the success in time and space necessitates the discovery and development of foundational principles to guide the design of generic artificial intelligence algorithms. FINPenn will support a class of scholar trainees along with a class of visiting postdocs and students to advance this agenda. The center will engage the community through the organization of workshops and lectures and will disseminate knowledge with onsite and online educational activities at the undergraduate and graduate level.FINPenn builds on two observations: (i) To understand the foundations of data science it is necessary to succeed beyond Euclidean signals in time and space. This is true even to understand the foundations for Euclidean signal processing. (ii) Humans live in Euclidean time and space. To succeed in information processing beyond signals with Euclidean structure, operation from foundational principles is necessary because human intuition is of limited help. For instance, convolutional neural networks have found success in the processing of images and signals in time but they rely heavily on spatial and temporal intuition. To generalize their success to unconventional signal domains it is necessary to postulate fundamental principles and generalize from those principles. If the generalizations are successful they not only illuminate the new application domains but they also help establish the validity of the postulated principles for Euclidean spaces in the tradition of predictive science. The proposers further contend that the foundational principles of data sciences are to be found in the exploitation of structure and the associated invariances and symmetries that structure generates. The initial focus of the center is in advancing the theory of information processing in signals whose structure is defined by a group, a graph, or a topology. These three types of signals generate three foundational research directions which build on the particular strengths of the University of Pennsylvania on network sciences, robotics, and autonomous systems which are areas in which these types of signals appear often. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.

人工智能的最新进展使我们从图像和时间序列中提取信息的能力取得了重大进展。保持这种进展速度取决于在处理更复杂的信号时获得同样重要的结果，例如那些由自治系统和连接设备网络获取的信号，或者那些在复杂的生物和社会系统研究中出现的信号。该奖项建立了FINPenn，即宾夕法尼亚大学信息处理基础中心。该中心的重点是建立基础理论，使研究数据超越时间和图像。该中心的前提是，人类对空间和时间的丰富直观理解可能不一定适用于复杂信号的处理。因此，在时间和空间上的成功匹配需要发现和发展基本原则来指导通用人工智能算法的设计。 FINPenn将沿着一班学者学员以及一班访问博士后和学生来推进这一议程。该中心将通过组织研讨会和讲座吸引社区参与，并将通过本科生和研究生阶段的现场和在线教育活动传播知识。FINPenn建立在两个观察基础上：（i）为了理解数据科学的基础，有必要在时间和空间上超越欧几里得信号。这是真实的，甚至理解欧几里得信号处理的基础。(ii)人类生活在欧几里得时空中。要成功地处理具有欧几里德结构的信号之外的信息，必须根据基本原理进行操作，因为人类直觉的帮助有限。例如，卷积神经网络在及时处理图像和信号方面取得了成功，但它们严重依赖于空间和时间直觉。为了将它们的成功推广到非常规信号领域，有必要假设基本原理并从这些原理中进行推广。如果推广是成功的，他们不仅照亮了新的应用领域，但他们也有助于建立有效性的假设原则的欧几里得空间的传统预测科学。 数据科学的基本原则可以在结构的开发以及结构产生的相关不变性和对称性中找到。该中心最初的重点是推进信号的信息处理理论，其结构由群，图或拓扑结构定义。这三种类型的信号产生了三个基础研究方向，这些方向建立在宾夕法尼亚大学在网络科学、机器人和自主系统方面的特殊优势之上，这些领域是这些类型的信号经常出现的领域。该项目是美国国家科学基金会利用数据革命（HDR）大创意活动的一部分。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces

• DOI: 10.48550/arxiv.2206.08362
• 发表时间: 2022-06
• 期刊: ArXiv
• 作者: Yinshuang Xu;Jiahui Lei;Edgar Dobriban;Kostas Daniilidis

Graphon Signal Processing

• DOI: 10.1109/tsp.2021.3106857
• 发表时间: 2020-03
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Luana Ruiz;Luiz F. O. Chamon;Alejandro Ribeiro

Graphon Neural Networks and the Transferability of Graph Neural Networks

• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Luana Ruiz;Luiz F. O. Chamon;Alejandro Ribeiro

Learning Augmentation Distributions using Transformed Risk Minimization

• 发表时间: 2021-11
• 期刊: ArXiv
• 作者: Evangelos Chatzipantazis;Stefanos Pertigkiozoglou;Edgar Dobriban;Kostas Daniilidis

Probably Approximately Correct Constrained Learning

• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Luiz F. O. Chamon;Alejandro Ribeiro