Publications: Recent submissions

Now showing items 103-105 of 160

    • Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality? 

      Poggio, Tomaso; Mhaskar, Hrushikesh; Rosasco, Lorenzo; Miranda, Brando; Liao, Qianli (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-11-23)
      [formerly titled "Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review"] The paper reviews and extends an emerging body of theoretical results on deep learning including the ...

    • 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 ...