The MILAN project, a joint effort involving Arizona State University and New York University, has produced and validated fundamental techniques for the realization of efficient, r...
We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
We present cdec, an open source framework for decoding, aligning with, and training a number of statistical machine translation models, including word-based models, phrase-based m...
Chris Dyer, Adam Lopez, Juri Ganitkevitch, Jonatha...
Activity recognition based on data from mobile wearable devices is becoming an important application area for machine learning. We propose a novel approach based on a combination ...
Lifecycle information can be of great help in the retrieval, authoring and usage of both, Learning Resources and Knowledge Documents. In my PhD thesis I want to show how the differ...