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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

  • Poggio, Tomaso; Liao, Qianli (Center for Brains, Minds and Machines (CBMM), arXiv, 2017-03-30)
    Previous theoretical work on deep learning and neural network optimization tend to focus on avoiding saddle points and local minima. However, the practical observation is that, at least for the most successful Deep ...
  • Lotter, William; Kreiman, Gabriel; Cox, David (Center for Brains, Minds and Machines (CBMM), arXiv, 2017-03-01)
    While great strides have been made in using deep learning algorithms to solve supervised learning tasks, the problem of unsupervised learning—leveraging unlabeled examples to learn about the structure of a domain — remains ...
  • Tachetti, Andrea; Voinea, Stephen; Evangelopoulos, Georgios (Center for Brains, Minds and Machines (CBMM), arXiv, 2017-03-13)
    The complexity of a learning task is increased by transformations in the input space that preserve class identity. Visual object recognition for example is affected by changes in viewpoint, scale, illumination or planar ...
  • 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 ...