Machine learning to understand images and text

机器学习研究的目标是建立从数据中提取有用信息的计算机系统。 在学习的两个主要领域取得了相当大的进展。受监督的方法，它们依赖于 对于每个输入具有目标标签，已被证明是非常强大和广泛适用的， 尤其是在分类问题上，其中每个输入样本被分配给一个小集合中的一个 所有的课程。这些方法通常不能很好地适应训练实例和类的数量。 非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，但不像 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。 这项提议的重点是开发同时利用标记和非标记的学习方法 例如，解决两个基本问题。第一个是提取信息性摘要 结构化对象，其中包含多个相互依赖的组件。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，它需要挑选 视觉输入中的多个项目。例如，一条繁忙街道的视频以数字形式记录为 彩色像素地图可以用汽车和行人及其运动来重新表示。这是 这是一个基本而困难的问题，涉及低级别图像属性与 高级的、特定于对象的知识。 第二个问题是快速学习。人类从少数几个人那里获得视觉概念的能力 示例，以及识别复杂场景中的实例，对以下领域提出了中心挑战 计算机视觉、认知科学和机器学习。提高机器获取新产品的能力 概念不仅易于应用于视觉，还能够分析包含相对 新奇的对象，还可以在线交互领域，作为一个快速学习系统可以构建一个 只有几个问题或对用户的上网了解有限的用户的准确模型 历史或产品偏好。

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.