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

传统的数据分析方法是以数据生成过程的数学、物理或统计模型为基础的，这些模型是在假设数据相对干净并为特定任务收集的情况下制定的。在过去的几十年里，数据采集的进步导致了大量的、嘈杂的、高维的数据集，这些数据集不一定是为特定的任务收集的。这导致了数据驱动方法的出现，例如深度学习，它使用大量的标记数据来学习“黑盒”模型，这些模型不提供对建模过程的明确描述。这种数据驱动的方法已经导致了计算机视觉和语音识别中的应用的模式识别系统的性能的显着改善，其中可以生成大量的标记数据。然而，现有的模型不是很好解释，它们的预测对对抗性扰动不鲁棒。此外，在科学和工程领域的许多应用中，数据标记的成本非常高，解释模型预测和估计不确定性的能力至关重要。为了应对这些挑战，将在约翰霍普金斯大学建立一个关于数据科学理论基础的TRIPODS研究所。该研究所的目标将是：（1）为下一代数据分析方法奠定基础，该方法将整合基于模型和数据驱动的方法，（2）通过每月的系列研讨会，学期的研究主题，年度研究研讨会以及夏季研究学校和数据科学基础研讨会，促进数据科学家之间的互动，以及（3）在数据科学基础上创建新的本科生和研究生课程。该研究所汇集了数学家，统计学家，理论计算机科学家和电气工程师的多学科团队，他们在机器学习，深度学习，统计学习和图形推理，优化，近似理论，信号处理、动力系统和控制，为下一代数据分析方法奠定基础，这将整合基于模型和数据驱动的方法。特别是，该研究所将专注于研究深度神经模型的基础（例如，前馈网络、递归网络、生成对抗网络）和结构化数据的生成模型（例如，图形模型，随机图，动态系统），最终目标是达到更可解释，对扰动鲁棒，并且可以在最少的监督下学习的集成模型。第一阶段研究所的目标是（1）研究前馈网络的泛化，优化和近似特性，（2）发展统计推断和学习的基础，以及（3）研究深度网络和图形的集成，以学习结构化数据集之间的映射。 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。

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.