Machine learning to understand images and text

机器学习研究旨在构建从数据中提取有用信息的计算机系统。 在两个主要学习领域取得了相当大的进展。 监督方法，依赖于 对每个输入都有目标标签，已经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给一个小集合中的一个 的阶级。 这些方法通常随着训练示例和类的数量而扩展性较差。 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 这些表示对许多输入-输出映射都很有用。 这些方法容易扩展，但不像 强大，因为没有标签，必须施加相当大的结构才能使算法工作。 这个建议的重点是发展的学习方法，利用标记和未标记的 例子，解决两个基本问题。 第一个是提取信息摘要， 结构化对象，其中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，它需要挑选出 视觉输入中的多个项目。 例如，数字记录为视频流的忙碌街道的视频， 彩色像素图可以根据汽车和行人及其运动来重新表示。这是 一个基本而困难的问题，涉及到低层次的图像属性与 高层次、特定于对象的知识。 第二个问题是快速学习。 人类从少数人身上获得视觉概念的能力 实例，并在复杂场景中识别实例，对以下领域提出了核心挑战： 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 概念不仅适用于视觉，还可以分析包含相对 新颖的对象，而且还可以在线交互领域，作为一个快速学习系统可以建立一个 一个用户的精确模型，只有几个问题，或有限的知识的人的网页冲浪 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.