Probable Dataset Searching Method with Uncertain Dataset Information in Adjusting Architecture Hyper Parameter

Chen Yang, Jingyuan Wang

Abstract: Different types of tasks with uncertain dataset information are studied because different parts of data may have different difficulties to achieve. For example, in unsupervised learning and domain adaptation, datasets are provided without label information because of the cost of human annotation. In deep learning, adjusting architecture hyper parameters is important for the model performance and is also time consuming, so we try to adjust hyper parameters in two types of uncertain dataset information:1, dataset labels are postponed to be obtained so hyper parameters need to be adjusted without complete dataset information. 2, hyper parameters are adjusted with a subset training dataset since training models with complete training dataset is time consuming. Here, we propose several loss functions to search for probable dataset when the complete dataset information is not obtained. The experiments on 9 real world data demonstrate the performance of our method.

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