Browsing CBMM Memo Series by Subject "Hierarchy"

Now showing items 1-7 of 7

    • Can a biologically-plausible hierarchy e ectively replace face detection, alignment, and recognition pipelines? 

      Liao, Qianli; Leibo, Joel Z; Mroueh, Youssef; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-03-27)
      The standard approach to unconstrained face recognition in natural photographs is via a detection, alignment, recognition pipeline. While that approach has achieved impressive results, there are several reasons to be ...

    • Complexity of Representation and Inference in Compositional Models with Part Sharing 

      Yuille, Alan L.; Mottaghi, Roozbeh (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-05-05)
      This paper performs a complexity analysis of a class of serial and parallel compositional models of multiple objects and shows that they enable efficient representation and rapid inference. Compositional models are generative ...

    • Deep Convolutional Networks are Hierarchical Kernel Machines 

      Anselmi, Fabio; Rosasco, Lorenzo; Tan, Cheston; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2015-08-05)
      We extend i-theory to incorporate not only pooling but also rectifying nonlinearities in an extended HW module (eHW) designed for supervised learning. The two operations roughly correspond to invariance and selectivity, ...

    • A Deep Representation for Invariance And Music Classification 

      Zhang, Chiyuan; Evangelopoulos, Georgios; Voinea, Stephen; Rosasco, Lorenzo; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-17-03)
      Representations in the auditory cortex might be based on mechanisms similar to the visual ventral stream; modules for building invariance to transformations and multiple layers for compositionality and selectivity. In this ...

    • I-theory on depth vs width: hierarchical function composition 

      Poggio, Tomaso; Anselmi, Fabio; Rosasco, Lorenzo (Center for Brains, Minds and Machines (CBMM), 2015-12-29)
      Deep learning networks with convolution, pooling and subsampling are a special case of hierar- chical architectures, which can be represented by trees (such as binary trees). Hierarchical as well as shallow networks can ...

    • Learning Real and Boolean Functions: When Is Deep Better Than Shallow 

      Mhaskar, Hrushikesh; Liao, Qianli; Poggio, Tomaso (Center for Brains, Minds and Machines (CBMM), arXiv, 2016-03-08)
      We describe computational tasks - especially in vision - that correspond to compositional/hierarchical functions. While the universal approximation property holds both for hierarchical and shallow networks, we prove that ...

    • Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency 

      Lu, Wenhao; Lian, Xiaochen; Yuille, Alan L. (Center for Brains, Minds and Machines (CBMM), arXiv, 2014-06-13)
      This paper addresses the problem of semantic part parsing (segmentation) of cars, i.e.assigning every pixel within the car to one of the parts (e.g.body, window, lights, license plates and wheels). We formulate this as a ...