Now showing items 1-5 of 5

    • Active Learning and Submodular Functions 

      Guillory, Andrew Russell (2012-09-13)
      Active learning is a machine learning setting where the learning algorithm decides what data is labeled. Submodular functions are a class of set functions for which many optimization problems have efficient exact or ...
    • Bayesian Computation and Optimal Decision Making in Primate Brains 

      Huang, Yanping
      This dissertation investigates the computational principles underlying the brains’ remarkable capacity to perceive, learn and act in environments of constantly varying uncertainty. Bayesian probability theory has suggested ...
    • Entity Analysis with Weak Supervision: Typing, Linking, and Attribute Extraction 

      Ling, Xiao
      With the advent of the Web, textual information has grown at an explosive rate. To digest this enormous amount of data, an automatic solution, Information Extraction (IE), has become necessary. Information extraction is a ...
    • Object Recognition and Semantic Scene Labeling for RGB-D Data 

      Lai, Kevin Kar Wai (2014-02-24)
      The availability of RGB-D (Kinect-like) cameras has led to an explosive growth of research on robot perception. RGB-D cameras provide high resolution (640 x 480) synchronized videos of both color (RGB) and depth (D) at 30 ...
    • Situated Learning and Understanding of Natural Language 

      Artzi, Yoav
      Robust language understanding systems have the potential to transform how we interact with computers. However, significant challenges in automated reasoning and learning remain to be solved before we achieve this goal. To ...