Usually, the tuning method used commonly is to select the optimal combination of the values with the largest performance measure(called direct tuning method). However, this method has poor robustness. Hence, this paper proposed a robust hyper-parameter tuning method. Specifically, taking the tuning of SGNS(skip-gram with negative-sample) algorithm as an example in word analogy task, it drew the conclusions as follow. Five of all seven hyper-parameters in SGNS that had significant influence on the performance were determined as the control factors and remained two as the noise factors, and three of the five control factors had significant influence on the variance of the performance measure after ANOVA for experimental data. Therefore, direct selection the optimal combination only by maximum expectation was not reasonable. There was no significant difference in the prediction accuracy between robust tuning method and direct tuning method, but robust tuning method remarkably had good robustness. The robust tuning method had practical referential value for tuning hyper-parameters of deep neural networks.

[2021-02-05] Printed Article /doc/2019.12.0672/v2