Machine learning to understand images and text

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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.