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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

  • Subirana, Brian; Bagiati, Aikaterini; Sarma, Sanjay (Center for Brains, Minds and Machines (CBMM), 2017-06-20)
    How important are Undergraduate College Academics after graduation? How much do we actually remember after we leave the college classroom, and for how long? Taking a look at major University ranking methodologies one can ...
  • Volokitin, Anna; Roig, Gemma; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2017-06-26)
    Crowding is a visual effect suffered by humans, in which an object that can be recognized in isolation can no longer be recognized when other objects, called flankers, are placed close to it. In this work, we study the ...
  • Anselmi, Fabio; Evangelopoulos, Georgios; Rosasco, Lorenzo; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), 2017-05-26)
    The properties of a representation, such as smoothness, adaptability, generality, equivari- ance/invariance, depend on restrictions imposed during learning. In this paper, we propose using data symmetries, in the sense of ...
  • Cheney, Nicholas; Schrimpf, Martin; Kreiman, Gabriel (Center for Brains, Minds and Machines (CBMM), arXiv, 2017-04-03)
    Deep convolutional neural networks are generally regarded as robust function approximators. So far, this intuition is based on perturbations to external stimuli such as the images to be classified. Here we explore the ...
  • Zhang, Chiyuan; Liao, Qianli; Rakhlin, Alexander; Sridharan, Karthik; Miranda, Brando; Golowich, Noah; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), 2017-04-04)
    In Theory III we characterize with a mix of theory and experiments the generalization properties of Stochastic Gradient Descent in overparametrized deep convolutional networks. We show that Stochastic Gradient Descent (SGD) ...
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