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Next generation deep neural networks for classification hosted on embeddedplatforms will rely on fast, efficient, and accurate learning algorithms.Initialization of weights in learning networks has a great impact on theclassification accuracy. In this paper we focus on deriving good initialweights by modeling the error function of a deep neural network as ahigh-dimensional landscape. We observe that due to the inherent complexity inits algebraic structure, such an error function may conform to general resultsof the statistics of large systems. To this end we apply some results fromRandom Matrix Theory to analyse these functions. We model the error function interms of a Hamiltonian in N-dimensions and derive some theoretical resultsabout its general behavior. These results are further used to make betterinitial guesses of weights for the learning algorithm.

On the Modeling of Error Functions as High Dimensional Landscapes for Weight Initialization in Learning Networks