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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: | https://mu.eprints-hosting.org/id/eprint/5484 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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