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    Differentiating Functions of the Jacobian with Respect to the Weights


    Flake, Gary William and Pearlmutter, Barak A. (2000) Differentiating Functions of the Jacobian with Respect to the Weights. Advances in Neural Information Processing Systems, 12. pp. 435-441. ISSN 1049-5258

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    Abstract

    For many problems, the correct behavior of a model depends not only on its input-output mapping but also on properties of its Jacobian matrix, the matrix of partial derivatives of the model's outputs with respect to its inputs. We introduce the J-prop algorithm, an efficient general method for computing the exact partial derivatives of a variety of simple functions of the Jacobian of a model with respect to its free parameters. The algorithm applies to any parametrized feedforward model, including nonlinear regression, multilayer perceptrons, and radial basis function networks.

    Item Type: Article
    Keywords: Differentiating Functions; Jacobian; Respect to the Weights;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 5484
    Depositing User: Barak Pearlmutter
    Date Deposited: 13 Oct 2014 15:16
    Journal or Publication Title: Advances in Neural Information Processing Systems
    Publisher: Massachusetts Institute of Technology Press (MIT Press)
    Refereed: Yes
    URI:

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