HDR TRIPODS: Institute for the Foundations of Graph and Deep Learning

HDR TRIPODS：图形和深度学习基础研究所

• 批准号: 1934979
• 负责人: Rene Vidal
• 金额: $ 150万
• 依托单位: Johns Hopkins University
• 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: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934979&HistoricalAwards=false
• 关键词: HDR; TRIPODS; Institute; Foundations; Graph; HDR; TRIPODS; Institute; Foundations; Graph

Classical data-analysis methods were based on mathematical, physical or statistical models for the data-generation process, which were developed under the assumption that the data were relatively clean and collected for a specific task. Over the past few decades, advances in data acquisition have led to massive, noisy, high-dimensional datasets, which were not necessarily collected for a specific task. This has lead to the emergence of data-driven methods, such as deep learning, which use massive amounts of labeled data to learn 'black-box' models, which do not provide an explicit description of the process being modeled. Such data-driven methods have led to dramatic improvements in the performance of pattern-recognition systems for applications in computer vision and speech recognition for which massive amounts of labeled data can be generated. However, existing models are not very interpretable, and their predictions are not robust to adversarial perturbations. Moreover, there are many applications in science and engineering where data labeling is extremely costly, and the ability to interpret model predictions and produce estimates of uncertainty is essential. To address these challenges, a TRIPODS Institute on the Theoretical Foundations of Data Science will be created at Johns Hopkins University. The goals of the institute will be to (1) develop the foundations for the next generation of data analysis methods, which will integrate model-based and data-driven approaches, (2) foster interactions among data scientists through a monthly seminar series, semester-long research themes, an annual research symposium, and a summer research school and workshop on the foundations of data science, and (3) create new undergraduate and graduate curricula on the foundations of data science.The institute brings together a multidisciplinary team of mathematicians, statisticians, theoretical computer scientists, and electrical engineers with expertise in the foundations of machine learning, deep learning, statistical learning and inference on graphs, optimization, approximation theory, signal processing, dynamical systems and controls, to develop the foundations for the next generation of data-analysis methods, which will integrate model-based and data-driven approaches. In particular, the institute will focus on studying the foundations of deep neural models (e.g., feedforward networks, recurrent networks, generative adversarial networks) and generative models of structured data (e.g., graphical models, random graphs, dynamical systems), with the ultimate goal of arriving at integrated models that are more interpretable, robust to perturbations, and learnable with minimal supervision. The goals of the Phase I Institute will be to (1) study generalization, optimization and approximation properties of feedforward networks, (2) develop the foundations of statistical inference and learning on and of graphs, and (3) study the integration of deep networks and graphs for learning maps between structured datasets. 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.

经典数据分析方法基于数​​据生成过程的数学，物理或统计模型，这些模型是根据数据相对清洁并收集到特定任务的假设开发的。在过去的几十年中，数据获取的进步导致了大量，嘈杂，高维数据集，这些数据集不一定是针对特定任务收集的。这导致了数据驱动方法的出现，例如深度学习，这些方法使用大量标记的数据来学习“黑盒”模型，这些模型并未对所建模的过程提供明确的描述。这种数据驱动的方法导致了用于计算机视觉和语音识别的应用程序应用程序的性能的显着改善，可以生成大量标记的数据。但是，现有模型不是很容易解释的，它们的预测对对抗性扰动并不强大。此外，在科学和工程中，数据标记的成本非常高，解释模型预测和产生不确定性估计的能力至关重要。为了应对这些挑战，将在约翰·霍普金斯大学（Johns Hopkins University）创建有关数据科学理论基础的三脚架研究所。该研究所的目标将是（1）为下一代数据分析方法开发基础，该方法将通过每月的课程系列，学期的研究主题，年度研究研讨会以及夏季研究学校以及关于数据科学的基础，并创建了新的基础研究，并创建了新的研究，并创建了新的研究，并创建了新的研究，并创建了新的研究，并创建了新的研究，并创建了新的研究，并将新的研究学院和新的工作介绍（3）研究所汇集了一个数学家，统计学家，理论计算机科学家和电气工程师的多学科团队，在机器学习，深度学习，统计学习和图形，优化，近似理论，信号处理，动态系统和控制方面的基础上具有专业知识，以开发出数据es基于数据的模型和数据的基础。特别是，该研究所将重点研究深度神经模型的基础（例如，前馈网络，经常性网络，生成的对手网络）和结构化数据的生成模型（例如，图形模型，随机图，动态图，动力学系统），具有在集成模型上的最终目标，可在集成模型中具有更明显的可解释，可与可解释的，可与既有的监督，分钟可观且学习。 I阶段研究所的目标是（1）研究概述网络的概括，优化和近似特性，（2）在图和图上开发统计推断和学习的基础，以及（3）研究深网和图的整合以学习结构化数据集之间的学习图。 该项目是国家科学基金会利用数据革命（HDR）的大创意活动的一部分。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来支持。