Center for Brains, Minds & Machines: Recent submissions

Now showing items 103-105 of 159

    • Where do hypotheses come from? 

      Dasgupta, Ishita; Schulz, Eric; Gershman, Samuel J. (Center for Brains, Minds and Machines (CBMM), 2016-10-24)
      Why are human inferences sometimes remarkably close to the Bayesian ideal and other times systematically biased? One notable instance of this discrepancy is that tasks where the candidate hypotheses are explicitly available ...

    • Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning 

      Liao, Qianli; Kawaguchi, Kenji; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-10-19)
      We systematically explored a spectrum of normalization algorithms related to Batch Normalization (BN) and propose a generalized formulation that simultaneously solves two major limitations of BN: (1) online learning and ...

    • Anchoring and Agreement in Syntactic Annotations 

      Berzak, Yevgeni; Huang, Yan; Barbu, Andrei; Korhonen, Anna; Katz, Boris (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-09-21)
      Published in the Proceedings of EMNLP 2016 We present a study on two key characteristics of human syntactic annotations: anchoring and agreement. Anchoring is a well-known cognitive bias in human decision making, where ...