Guiding Neural Network Initialization via Marginal Likelihood Maximization

• URL: http://arxiv.org/abs/2012.09943v1
• Date: Thu, 17 Dec 2020 21:46:09 GMT
• Title: Guiding Neural Network Initialization via Marginal Likelihood Maximization
• Authors: Anthony S. Tai, Chunfeng Huang
• Abstract summary: We leverage the relationship between neural network and Gaussian process models having corresponding activation and covariance functions to infer the hyper- parameter values. Our experiment shows that marginal consistency provides recommendations that yield near-optimal prediction performance on MNIST classification task.
• Score: 0.9137554315375919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a simple, data-driven approach to help guide hyperparameter selection for neural network initialization. We leverage the relationship between neural network and Gaussian process models having corresponding activation and covariance functions to infer the hyperparameter values desirable for model initialization. Our experiment shows that marginal likelihood maximization provides recommendations that yield near-optimal prediction performance on MNIST classification task under experiment constraints. Furthermore, our empirical results indicate consistency in the proposed technique, suggesting that computation cost for the procedure could be significantly reduced with smaller training sets.

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