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Center for Brains, Minds & Machines

Research and Teaching Output of the MIT Community

Center for Brains, Minds & Machines


The Center for Brains, Minds and Machines (CBMM) is a National Science Foundation funded Science and Technology Center on the interdisciplinary study of intelligence. This effort is a multi-institutional collaboration headquartered at the McGovern Institute for Brain Research at MIT, with Harvard University as a managing partner. Visit the CBMM website for more information.

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Recent Submissions

  • Ben-Yosef, Guy; Assif, Liav; Ullman, Shimon (Center for Brains, Minds and Machines (CBMM), 2017-02-08)
    The goal in this work is to model the process of ‘full interpretation’ of object images, which is the ability to identify and localize all semantic features and parts that are recognized by human observers. The task is ...
  • Mlynarski, Wiktor; McDermott, Josh (Center for Brains, Minds and Machines (CBMM), arXiv, 2017-01-25)
    Interaction with the world requires an organism to transform sensory signals into representations in which behaviorally meaningful properties of the environment are made explicit. These representations are derived through ...
  • Harari, Daniel; Gao, Tao; Kanwisher, Nancy; Tenenbaum, Joshua; Ullman, Shimon (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-11-28)
    Humans are remarkably adept at interpreting the gaze direction of other individuals in their surroundings. This skill is at the core of the ability to engage in joint visual attention, which is essential for establishing ...
  • Poggio, Tomaso; Mhaskar, Hrushikesh; Rosasco, Lorenzo; Miranda, Brando; Liao, Qianli (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-11-23)
    The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. A class of deep convolutional networks ...
  • Dasgupta, Ishita; Schulz, Eric; Gershman, Samuel J. (Center for Brains, Minds and Machines (CBMM), 2016-10-24)
    Why are human inferences sometimes remarkably close to the Bayesian ideal and other times systematically biased? One notable instance of this discrepancy is that tasks where the candidate hypotheses are explicitly available ...