Advanced Search
DSpace@MIT

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.

Sub-communities within this community

Recent Submissions

  • 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), arXiv.org, 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 ...
  • Shen, Wei; Wang, Bin; Jiang, Yuan; Wang, Yan; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), 2017-10-01)
    In the field of connectomics, neuroscientists seek to identify cortical connectivity comprehensively. Neuronal boundary detection from the Electron Microscopy (EM) images is often done to assist the automatic reconstruction ...
  • Zhang, Chiyuan; Liao, Qianli; Rakhlin, Alexander; Miranda, Brando; Golowich, Noah; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), 2017-12-27)
    In Theory IIb we characterize with a mix of theory and experiments the optimization of deep convolutional networks by Stochastic Gradient Descent. The main new result in this paper is theoretical and experimental evidence ...
MIT-Mirage