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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

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    [Xiang et al.] Dynamic spatially smoothed regularization ensures boosting to select clustered but not scattered features.

    [Seeger.] Informax fMRI pulse seq. design, extends previous single 2d slice work to 3d volume optimization, which shows some improvement.

    [Berkes et al] No evidence of active sparsification in V1. They paralyzed some inputs of V1, the result mismatches sparse model prediction.

    [Zinkevich] An asynchronous parallel SGD implementation showed that delayed propagation of new gradient leads to faster convergence.

    [Cavagnaro] (standard D-opt) experiment design for human memory experiment. Compared 3 different short-term memory model.

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