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We propose a simple, data-driven approach to help guide hyperparameterselection for neural network initialization. We leverage the relationshipbetween neural network and Gaussian process models having correspondingactivation and covariance functions to infer the hyperparameter valuesdesirable for model initialization. Our experiment shows that marginallikelihood maximization provides recommendations that yield near-optimalprediction performance on MNIST classification task under experimentconstraints. Furthermore, our empirical results indicate consistency in theproposed technique, suggesting that computation cost for the procedure could besignificantly reduced with smaller training sets.

Guiding Neural Network Initialization via Marginal Likelihood Maximization