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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

  • Arend, Luke; Han, Yena; Schrimpf, Martin; Bashivan, Pouya; Kar, Kohitij; Poggio, Tomaso; DiCarlo, James J.; Boix, Xavier (Center for Brains, Minds and Machines (CBMM), 2018-11-02)
    Deep neural networks have been shown to predict neural responses in higher visual cortex. The mapping from the model to a neuron in the brain occurs through a linear combination of many units in the model, leaving open the ...
  • Xiao, Will; Chen, Honglin; Liao, Qianli; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), 2018-09-27)
    The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feed- back pathways. To address ...
  • Liao, Qianli; Miranda, Brando; Hidary, Jack; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), 2018-07-11)
    Deep networks are usually trained and tested in a regime in which the training classification error is not a good predictor of the test error. Thus the consensus has been that generalization, defined as convergence of the ...
  • Poggio, Tomaso; Liao, Qianli; Miranda, Brando; Burbanski, Andrzej; Hidary, Jack (Center for Brains, Minds and Machines (CBMM),, 2018-06-29)
    A main puzzle of deep neural networks (DNNs) revolves around the apparent absence of "overfitting", defined in this paper as follows: the expected error does not get worse when increasing the number of neurons or of ...
  • Shen, Wei; Guo, Yilu; Wang, Yan; Zhao, Kai; Wang, Bo; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), 2018-06-01)
    Age estimation from facial images is typically cast as a nonlinear regression problem. The main challenge of this problem is the facial feature space w.r.t. ages is inhomogeneous, due to the large variation in facial ...