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The goal of the MPLab is to develop systems that perceive and interact with humans in real time using natural communication channels. To this effect we are developing perceptual primitives to detect and track human faces and to recognize facial expressions. We are also developing algorithms for robots that develop and learn to interact with people on their own. Applications include personal robots, perceptive tutoring systems, and system for clinical assessment, monitoring, and intervention.

  • Introduction to the MPLab (PDF)
  • MPLAB 5 Year Progress Report (PDF)

  • NEWS

    [Bengio et al.] class dependent feature selection, similar to “personal spam filter”. Mixed norm regularization generate sparses features.

    [Schmidt] blind source separation with arbitrary linear/equality constraint on source. Solved using MCMC

    Vul gave a talk on an ideal observer model for multiple object tracking using particle filters. Accounts for human experiments. Poster 2nite

    Graph-based consensus maximization: incorporate grouping constraints with outputs of classification algorithms

    Hinton gave a talk on extending RBMs to higher order interactions and applying it to a range problems with impressive results.

    Hsu and Griffith use generative versus discriminative models of language learning to help give insight into the debate on nativism of lang.

    [Zoran and Weiss] reported that edge filter could be obtained from natural images using maximum tree dependency algorithms(nonsparse code!).

    [Ouyang and David] An Bayesian framework for realtime handdrawn sketch recognition with “component recog./context/continuity” likelihood.

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