Approximate Nearest Neighbors Methods for Learning and VisionTrevor Darrell, Piotr Indyk, Greg Shakhnarovich
(MIT Computer Science and Artificial Intelligence Lab)
Paul Viola (Microsoft Research)
Example-based methods have been employed in machine learning for decades, and shown good performance. Their main drawback is the computational intensity of the search for nearest neighbors as the number of examples and especially the dimension of the example space become large. This has significantly diminished the appeal of these methods for modern problems, which deal with very large amounts of high-dimensional data. Computer vision is an area in which this is particularly true.
Recent research efforts in the theoretical and algorithmic community have produced algorithms for very fast approximate similarity search, which have the potential of making example-based learning feasible again. There has been growing interest in the learning and vision community in this topic, and in the last two years some published results have started to appear.
The main goal of this workshop is to try to formulate and analyze research agenda in this new area, and to identify important directions in which related research will move in the following years. By bringing together researchers from the communities working on machine learning, vision and theory we aim at developing common understanding of the potential and limitations of the newly available tools, what problems can benefit from these tools, and share the experience and insights that have been accumulating in the past few years.
IssuesBelow is a non-exhaustive list of issues we hope to address at the workshop. Some of the topics have seen considerable attention, some are largely open.
Format and schedule
The schedule of the workshop is posted here. We plan to divide the time for this workshop roughly evenly between theory, learning, and perception. The time will be divided between presentations, moderated discussion and possibly a poster viewing session. Please contact the organizers if you would like to present a poster in such session.
gregory @ ai.mit.edu