CAREER: Co-evolution of Machine Intelligence and Continuous Information

现实世界是复杂的、不断变化的。随着时间的推移，通过连续的噪声数据流，信息逐渐变得可用。例如，分子生物学数据库总是更新各种新的基因-蛋白质关系(例如，基因调控、遗传相互作用)，在湿法实验室实验中得到验证。天体粒子物理学中的高能实验产生了以连续的大流量流的形式的大量数据。人际互动行为(例如，面部表情、手势)包含的节奏不仅在时间上相关，而且在持续的相互影响流中表现出相位同步。为了充分利用这些在线和实时的非静态数据流，该项目旨在建立能够量化不确定性、不断适应新信息并同时巩固先前获得的知识的机器智能。该项目特别受到计算生物学、天文物理学和人类交互行为应用的推动，并将导致创新的在线推理方法，这些方法广泛适用于数据密集型领域。此外，这项研究将支持残疾、妇女和少数族裔学生群体的跨学科发展，以及研究生水平机器学习课程的发展。推广工作将有助于增加学术界和产业界之间的合作伙伴关系，并提高公众的科学素养。总体研究目标是创建一个基于贝叶斯非参数的统一动态统计推理框架，以共同更新深度神经网络参数，调整神经结构，并巩固已有的知识。研究人员重点介绍了显著推进这项研究议程的三个相互关联的项目：(1)神经参数在线推理：设计和开发在线推理算法，以递归地更新具有连续数据流的神经网络参数的后验分布；(2)神经体系结构在线推理：创建建模和计算方法，使深层神经体系结构能够自动经历质的增长，以适应从新数据流动时逐渐可用的信息；以及(3)动态知识蒸馏：设计和开发建模方法，以合并旧知识和新知识，同时神经参数和体系结构都在发展，以从不同的数据流中提取统计结构。这些研究目标将得到多学科合作的补充，以严格验证和推广该框架。这项研究将为整个信息集成和数据科学带来高效的统计在线推理方法。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The real world is complex and constantly changing. Information becomes progressively available over time via continuous stream of noisy data. For example, molecular biology databases are always updated with various new gene-protein relationships (e.g., gene regulations, genetic interactions) validated in wet lab experiments. High-energy experiments in astro-particle physics give rise to large amounts of data in the form of continuous high-volume streams. Interpersonal interaction behaviors (e.g., facial expressions, gestures) contain rhythms that are not only correlated in time but also exhibit phase synchronization in an ongoing flow of mutual influence. In order to fully exploit these non-stationary streams of data online and in real-time, this project aims to build machine intelligence capable of quantifying uncertainty, constantly accommodating new information, and consolidating previously acquired knowledge in the meantime. The project is specifically motivated by applications to computational biology, astro-physics, and human interaction behaviors, and will result in innovative online inference methods that are broadly applicable across data-intensive domains. Furthermore, this research will support the interdisciplinary development of a diverse student body of disability, women, and minorities, and the development of graduate-level machine learning courses. The outreach work will contribute to increasing partnerships between academia and industry, and increasing public scientific literacy.The overarching research objective is to create a unified dynamic statistical inference framework based on Bayesian nonparametrics to jointly update deep neural network parameters, adapt neural architectures, and consolidate acquired knowledge. The investigator highlights three inter-related projects that significantly advance this research agenda: (1) neural parameter online inference: design and develop online inference algorithms to recursively update posterior distributions of neural network parameters with continuous data stream, (2) neural architecture online inference: create modeling and computational methods to enable deep neural architectures to automatically go through a qualitative growth to accommodate progressively available information from new data as they are streaming, and (3) dynamic knowledge distillation: design and develop modeling methods to merge old knowledge with new one while both neural parameters and architectures are evolving to extract statistical structures from heterogeneous data stream. These research aims will be complemented by multidisciplinary collaborations to rigorously validate and generalize the framework. This research will lead to efficient statistical online inference methods for the whole information integration and data science.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.