CAREER: Computation and Approximation in Structured Learning

职业：结构化学习中的计算和近似

• 批准号: 1054215
• 负责人: Ben Taskar
• 金额: $ 50万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1054215&HistoricalAwards=false
• 关键词: CAREER; Computation; Approximation; Structured; Learning; CAREER; Computation; Approximation; Structured; Learning

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.

机器学习正在改变许多领域从工程和科学到医学和业务的数据有意义的方式。机器学习已大大改善了语音识别，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信息的智能处理：该项目将着重于通过检测对象，人员，动作和之间的互动以及解析文档来自动描述视频，并通过提取实体，事件及其之间的关系来解析文档。 所有这些预测任务不仅需要真正的false或多项选择答案，而且需要考虑多数可能的答案。将这些联合预测分解为独立的决策（例如，单独翻译每个单词，一次识别音素，分别检测到每个对象）忽略了关键的相关性并导致准确性差。结构化模型，例如语法和图形模型，可以捕获强大的依赖性，但要相当多地计算成本。在这种结构化预测问题中提高准确性的障碍是推理的良好成本。 结构化的预测问题表现出近似误差和由于计算限制引起的推理误差之间的基本权衡，因为我们考虑了增加复杂性的模型。这种折衷的理解很少，但在机器学习应用中不断遇到。该项目的主要结果将是使用新颖的粗到精细体系结构来解决非常大规模的结构化预测的框架。该体系结构将实现对近似/计算权衡的明确，数据驱动的控制。 它有望极大地提高计算机视觉和自然语言应用中的最新准确性，并大大增强文档，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以及组织研讨会。