We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into...
Probabilistic grammars offer great flexibility in modeling discrete sequential data like natural language text. Their symbolic component is amenable to inspection by humans, while...
In this paper, we examine the application of manifold learning to the clustering problem. The method used is Locality Preserving Projections (LPP), which is chosen because of its ...
Hassan A. Kingravi, M. Emre Celebi, Pragya P. Raja...
We address the text-to-text generation problem of sentence-level paraphrasing — a phenomenon distinct from and more difficult than word- or phrase-level paraphrasing. Our appro...
Feature subset selection is important not only for the insight gained from determining relevant modeling variables but also for the improved understandability, scalability, and pos...