Machine learning to understand images and text

机器学习研究旨在建立从数据中提取有用信息的计算机系统。*在两个主要的学习领域取得了相当大的进步。监督方法依赖于每个输入都有目标标签，它已经被证明是非常强大和广泛适用的，特别是在分类问题上，其中每个输入示例被分配到一个小的类集。这些方法通常不适合训练示例和类的数量。无监督方法利用未标记的数据，其中没有提供目标，并试图构建对许多输入-输出映射有用的表示。这些方法很容易扩展，但不那么强大，因为没有标签，必须强加相当大的结构才能使算法工作。**本提案侧重于开发利用标记和未标记示例的学习方法，解决两个基本问题。首先是提取结构化对象的信息摘要，这些对象包含几个相互依赖的组件。两个非常常见的结构化对象*是图像和文档。该领域的一个示例任务是场景分析，这需要在视觉输入中挑选出多个项目。例如，将一条繁忙街道的视频以数字方式记录为彩色像素地图流，可以根据汽车、行人及其动作来重新表示。这是一个重要而困难的问题，涉及到低级图像属性与高级对象特定知识的结合。第二个问题是快速学习。人类从少数例子中获取视觉概念，并在复杂场景中识别实例的能力，对计算机视觉、认知科学和机器学习领域构成了核心挑战。提高机器获取新概念的能力不仅可以应用于视觉，使其能够分析包含相对新颖物体的场景，还可以应用于在线交互领域，因为快速学习系统可以通过几个问题，或者对用户上网历史或产品偏好的有限了解，建立一个准确的用户模型。

Machine learning research aims to build computer systems that extract useful information from data.*Considerable progress has been made in the two main areas of learning. Supervised methods, which rely*on having target labels for each input, have proved to be very powerful and widely applicable,*particularly on classification problems, in which each input example is assigned to one of a small set*of classes. These methods generally scale poorly with the number of training examples and classes.*Unsupervised methods utilize unlabeled data, where no targets are provided, and attempt to construct*representations useful for many input-output mappings. These methods scale readily but are not as*powerful, because without labels considerable structure must be imposed to make the algorithms work.**This proposal focuses on the development of learning methods that utilize both labeled and unlabeled*examples, addressing two fundamental problems. The first is extracting informative summaries of*structured objects, which contain several inter-dependent components. Two very common structured objects*are images and documents. A sample task in this domain is scene analysis, which entails picking out*multiple items in visual input. For example, a video of a busy street recorded digitally as a stream of*color pixel-maps may be re-represented in terms of the cars and pedestrians and their motions. This is*an essential and difficult problem, involving a combination of low-level image properties with*high-level, object-specific knowledge.**The second problem is rapid learning. The human ability to acquire a visual concept from a few*examples, and to recognize instances in a complex scene, poses a central challenge to the fields of*computer vision, cognitive science, and machine learning. Improving machines' ability to acquire new*concepts readily has applications not only to vision, enabling analysis of scenes containing relatively*novel objects, but also to the domain of online interaction, as a rapid learning system can build an*accurate model of a user with just a few questions, or limited knowledge of the person's web-surfing*history or product preferences.