We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...
This paper proposes a fast 3D reconstruction approach for efficiently generating watertight 3D models from multiple short baseline views. Our method is based on the combination of...
Mario Sormann, Christopher Zach, Joachim Bauer, Ko...
We investigate a method for learning object categories in a weakly supervised manner. Given a set of images known to contain the target category from a similar viewpoint, learning...
Over the last few years, two of the main research directions in machine learning of natural language processing have been the study of semi-supervised learning algorithms as a way...
This paper solves the open problem of extracting the maximal number of iterations from a loop that can be executed in parallel on chip multiprocessors. Our algorithm solves it opt...
Duo Liu, Zili Shao, Meng Wang, Minyi Guo, Jingling...