CBMM Memo Series: Recent submissions

Now showing items 61-63 of 149

    • Classical generalization bounds are surprisingly tight for Deep Networks 

      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 ...

    • Theory IIIb: Generalization in Deep Networks 

      Poggio, Tomaso; Liao, Qianli; Miranda, Brando; Burbanski, Andrzej; Hidary, Jack (Center for Brains, Minds and Machines (CBMM), arXiv.org, 2018-06-29)
      The general features of the optimization problem for the case of overparametrized nonlinear networks have been clear for a while: SGD selects with high probability global minima vs local minima. In the overparametrized ...

    • Deep Regression Forests for Age Estimation 

      Shen, Wei; Guo, Yilu; Wang, Yan; Zhao, Kai; Wang, Bo; e.a. (Center for Brains, Minds and Machines (CBMM), 2018-06-01)
      Age estimation from facial images is typically cast as a nonlinear regression problem. The main challenge of this problem is the facial feature space w.r.t. ages is inhomogeneous, due to the large variation in facial ...