EAGER-DynamicData: Principled and Scalable Probabilistic Frameworks for Dynamic Multi-modal Data

EAGER-DynamicData：动态多模态数据的有原则且可扩展的概率框架

• 批准号: 1462502
• 负责人: Lawrence Carin
• 金额: $ 10万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462502&HistoricalAwards=false
• 关键词: EAGER; DynamicData; Principled; Scalable; Probabilistic

Emergence of the Big Data phenomenon has given rise to data collections that are massive, highly heterogeneous and multi-modal, dynamically evolving, as well as incomplete, noisy and imprecise. These characteristics are becoming increasingly prevalent in data from a diverse range of domains, such as robotics, cognitive neuroscience, sensor generated data (e.g., in geoscience and remote sensing), and the dynamically evolving data on the web. The heterogeneity, complexity, dynamic evolution, and the often real-time processing requirements, call for methods that are both statistically rigorous as well as computationally scalable. Moreover, performing fast feature-extraction and/or predictions at *test time* is another key requirement, especially in problems involving dynamic data arriving at high speeds. This project will innovate on scalable statistical methods for learning from such massive dynamic multi-modal data, with a focus on designing novel probabilistic models for multi-layer latent feature extraction for such data. These multi-layer latent feature representations of the data will help capture the underlying dynamics and allow reconciling the data heterogeneity arising due to diverse data types and widely differing spatial and temporal resolutions across the different modalities, while also being useful for a wide range of fundamental data analysis tasks, such as classification, clustering, and predicting missing data. At the same time, the focus will also be on developing methods that are efficient at test time, so that fast feature extraction and predictions can be made in real time, to make these methods readily applicable to dynamic streaming data.This EArly Grant for Exploratory Research (EAGER) project endeavors to move beyond existing ad hoc approaches currently used for these problems, and develop a probabilistically grounded, statistically rigorous, and computationally scalable framework, based on Bayesian and nonparametric Bayesian modeling. Taking a Bayesian generative modeling approach will naturally enable modeling the dynamic behavior of the data and seamlessly integrate diverse types of data, while handling issues such as missingness, noise and the imprecise nature of the data. In addition, the nonparametric Bayesian treatment will provide the much-needed modeling flexibility and address many of the limitations of the existing Deep Learning models, e.g., by doing away with the need of extensive hand-tuning, incorporating rich prior knowledge about the model parameters, and allowing a natural sharing of statistical strength across the multiple data modalities. To handle the associated computational challenges, the framework will provide novel inference machinery in form of online Bayesian inference methods that will naturally handle dynamic, real-time data, and parallel and distributed Bayesian inference methods to handle massive multi-modal data that are too large for the capacity (storage and/or computational) of a single computing node. Furthermore, due to its inherent ability of quantifying model uncertainty, the proposed Bayesian framework will naturally facilitate a dynamic integration between model computation (inference) and data acquisition, and help design informed data acquisition (i.e., "active" sensing) methods in the context of dynamic multi-modal data. An overarching goal of this project is to also help synergize two important research directions in machine learning - nonparametric Bayesian methods and Deep Learning methods. By designing scalable nonparametric Bayesian solutions to the type of problems Deep Learning methods have been applied for, the project will convince the skeptics of Deep Learning methods to adopt these methods more openly. At the same time, the compelling range of problems and applications Deep Learning are being used for, will broaden the appeal of nonparametric Bayesian methods from a practical sense. We expect this synergy between these two areas will significantly advance the state-of-the-art in both areas.

大数据现象的出现引起了大量，高度异质和多模式，动态发展的数据收集，并且不完整，嘈杂和不精确。这些特征在来自各种域的数据中变得越来越普遍，例如机器人技术，认知神经科学，传感器生成的数据（例如，在地球科学和遥感中）以及网络上动态发展的数据。异质性，复杂性，动态演化以及通常实时的处理要求，要求在统计上既严格又可以进行计算可扩展的方法。此外，在 *测试时间 *执行快速的功能萃取和/或预测是另一个关键要求，尤其是在涉及高速到达的动态数据的问题中。该项目将根据可扩展的统计方法进行创新，以从这种大规模的动态多模式数据中学习，重点是设计用于此类数据的多层潜在特征提取的新型概率模型。这些数据的这些多层潜在特征表示将有助于捕获潜在的动态，并允许对不同模式的空间和时间分辨率进行调解，同时也对广泛的基本数据分析任务（例如分类，群集，预测缺少数据）有用。同时，重点也将放在开发测试时间有效的方法上，以便可以实时做出快速的提取和预测，以使这些方法易于适用于动态流数据。这项早期的探索性研究（急切）项目的早期赠款（急切）努力超越了当前使用的问题，并开发了这些问题，并在这些问题上开发了这些问题，并在这些问题上开发了稳定的范围，并在范围内进行了稳定的范围，并构成了统计的，并且是稳定的，并且是稳定的，并且是稳定的，并在统计上进行了稳定的范围，并将其范围置于范围内，并在统计上进行了稳定的范围。和非参数贝叶斯建模。采用贝叶斯生成建模方法将自然能够对数据的动态行为进行建模，并无缝整合多种类型的数据，同时处理诸如丢失，噪声和数据不精确的问题之类的问题。此外，非参数贝叶斯治疗将提供急需的建模灵活性，并解决现有深度学习模型的许多局限性，例如，通过不再需要大量的手工进行手工调整，结合了有关模型参数的丰富先验知识，并允许在多个数据模态上自然共享统计强度。为了应对相关的计算挑战，该框架将以在线贝叶斯推理方法的形式提供新颖的推理机械，这些方法将自然处理动态，实时数据，并行和分布式的贝叶斯推理方法，以处理单个计算发射器的容量（存储和/或计算）的大量多模式数据。此外，由于量化模型不确定性的固有能力，所提出的贝叶斯框架自然会促进模型计算（推理）和数据获取之间的动态整合，并帮助设计知情的数据获取（即“主动”传感）方法在动态多模式数据的上下文中。该项目的总体目标是帮助协同机器学习中的两个重要研究方向：非参数贝叶斯方法和深度学习方法。通过针对已应用深度学习方法类型的可扩展的非参数贝叶斯解决方案，该项目将使深度学习方法的怀疑论者更加公开地采用这些方法。同时，正在使用引人注目的问题和应用深度学习，将从实际意义上扩大非参数贝叶斯方法的吸引力。我们预计，这两个领域之间的这种协同作用将在这两个领域都大大提高最新。