Learning without local minima

A computationally efficient methodology for overcoming local minima in nonlinear neural network learning is presented. This methodology is based on the newly discovered TRUST global optimization paradigm. Enhancements to the backpropagation schema in feedforward multilayer architectures, and to adjoint-operator learning in recurrent networks are discussed. Extensions to TRUST now formally guarantee reaching a global minimum in the multidimensional case. Results for a standard benchmark are included, to illustrate the theoretical developments.<
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