CAREER: Computation and Approximation in Structured Learning

机器学习正在改变许多领域理解数据的方式，从工程和科学到医学和商业。机器学习极大地改进了语音识别、机器翻译、机器人导航和许多其他预测任务。机器学习的一个关键目标是自动化智能处理信息：该项目将专注于通过检测对象、人、动作和它们之间的交互来自动描述视频，并通过提取实体、事件和它们之间的关系来解析文档。所有这些预测任务需要的不仅仅是对错或多项选择的答案，而是需要考虑的可能答案的指数数量。将这些联合预测分解成独立的决定(例如，单独翻译每个单词，一次识别一个音素，单独检测每个对象)会忽略关键的相关性，导致准确性较低。结构化模型，如语法和图形模型，可以捕获强烈的依赖关系，但需要相当大的计算成本。在这样的结构化预测问题中，提高准确性的障碍是高昂的推理成本。当我们考虑日益复杂的模型时，结构化预测问题呈现出由于计算限制而导致的近似误差和推理误差之间的基本权衡。这一权衡很少被理解，但在机器学习应用中经常遇到。该项目的主要成果将是使用一种新的从粗到精的体系结构来处理超大规模结构化预测的框架。该体系结构将实现对近似/计算权衡的显式、数据驱动的控制。它承诺将极大地提高计算机视觉和自然语言应用程序中最先进的准确性，并极大地增强文档、图像和视频的搜索和组织。PI的计划包括在机器学习社区中发挥积极作用，通过教程、代码和数据传播结果，并组织研讨会。

Machine learning is transforming the way many fields make sense of data, from engineering and science to medicine and business. Machine learning has vastly improved speech recognition, machine translation, robotic navigation and many other prediction tasks. A crucial goal of machine learning is automating intelligent processing of information: this project will focus on automatically describing videos by detecting objects, people, actions and interactions between them, and parsing documents by extracting entities, events and relationships between them. All these prediction tasks require more than just true-false or multiple-choice answers, but have an exponential number of possible answers to consider. Breaking these joint predictions up into independent decisions (for example, translating each word on its own, recognizing a phoneme at a time, detecting each object separately) ignores critical correlations and leads to poor accuracy.Structured models, such as grammars and graphical models, can capture strong dependencies but at considerable computational costs. The barrier to improving accuracy in such structured prediction problems is the prohibitive cost of inference. Structured prediction problems present a fundamental trade-off between approximation error and inference error due to computational constraints as we consider models of increasing complexity. This trade-off is poorly understood but is constantly encountered in machine learning applications.The primary outcome of this project will be a framework for addressing very large scale structured prediction using a novel coarse-to-fine architecture. This architecture will enable explicit, data-driven control of the approximation/computation trade-off. It promises to drastically advance state-of-the-art accuracy in computer vision and natural language applications and greatly enhance search and organization of documents, images, and video. The PI's plan includes an active role in the machine learning community, disseminating results through tutorials, code and data and organizing workshops.