Publications: Recent submissions

Now showing items 61-63 of 160

    • An analysis of training and generalization errors in shallow and deep networks 

      Mhaskar, H.N.; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv.org, 2019-05-30)
      This paper is motivated by an open problem around deep networks, namely, the apparent absence of overfitting despite large over-parametrization which allows perfect fitting of the training data. In this paper, we analyze ...

    • Biologically-plausible learning algorithms can scale to large datasets 

      Xiao, Will; Chen, Honglin; Liao, Qianli; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv.org, 2018-11-08)
      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 feedback pathways. To address ...

    • The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors 

      O'Brien, Nicole; Latessa, Sophia; Evangelopoulos, Georgios; Boix, Xavier (Center for Brains, Minds and Machines (CBMM), 2018-11-01)
      The digital information age has generated new outlets for content creators to publish so-called “fake news”, a new form of propaganda that is intentionally designed to mislead the reader. With the widespread effects of the ...