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展。监督方法依赖于每个输入都有目标标签，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小组类中的一个。这些方法通常不适合训练示例和类的数量。无监督方法利用没有提供目标的未标记数据，并尝试构造对许多输入-输出映射有用的表示。这些方法很容易扩展，但没有那么强大，因为没有标签，必须强加相当大的结构才能使算法工作。本提案的重点是利用标记和未标记示例的学习方法的发展，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。这个领域的一个示例任务是场景分析，它需要在视觉输入中挑选出多个项目。例如，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、行人和他们的动作来重新表示。这是一个重要而困难的问题，涉及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。人类从几个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。提高机器轻松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.