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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.
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