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CBMM Memo Series

Research and Teaching Output of the MIT Community

CBMM Memo Series

 

Recent Submissions

  • Zhang, Mengmi; Feng, Jiashi; Lim, Joo Hwee; Zhao, Qi; Kreiman, Gabriel (Center for Brains, Minds and Machines (CBMM), arXiv.org, 2018-07-31)
    Can we infer intentions and goals from a person's actions? As an example of this family of problems, we consider here whether it is possible to decipher what a person is searching for by decoding their eye movement behavior. ...
  • Ben-Yosef, Guy; Kreiman, Gabriel; Ullman, Shimon (Center for Brains, Minds and Machines (CBMM), 2018-11-21)
    Objects and their parts can be visually recognized and localized from purely spatial information in static images and also from purely temporal information as in the perception of biological motion. Cortical regions have ...
  • 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 ...
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