Hyperparameter-free deep active learning for regression problems via query synthesis

• URL: http://arxiv.org/abs/2201.12632v1
• Date: Sat, 29 Jan 2022 18:41:08 GMT
• Title: Hyperparameter-free deep active learning for regression problems via query synthesis
• Authors: Simiao Ren, Yang Deng, Willie J. Padilla and Jordan Malof
• Abstract summary: We propose the first DAL query-synthesis approach for regression problems. We use the recently-proposed neural-adjoint (NA) solver to efficiently find points in the continuous input domain. We find that NA-QBC achieves better average performance than random sampling on every benchmark problem.
• Score: 5.572747615014008
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the past decade, deep active learning (DAL) has heavily focused upon classification problems, or problems that have some 'valid' data manifolds, such as natural languages or images. As a result, existing DAL methods are not applicable to a wide variety of important problems -- such as many scientific computing problems -- that involve regression on relatively unstructured input spaces. In this work we propose the first DAL query-synthesis approach for regression problems. We frame query synthesis as an inverse problem and use the recently-proposed neural-adjoint (NA) solver to efficiently find points in the continuous input domain that optimize the query-by-committee (QBC) criterion. Crucially, the resulting NA-QBC approach removes the one sensitive hyperparameter of the classical QBC active learning approach - the "pool size"- making NA-QBC effectively hyperparameter free. This is significant because DAL methods can be detrimental, even compared to random sampling, if the wrong hyperparameters are chosen. We evaluate Random, QBC and NA-QBC sampling strategies on four regression problems, including two contemporary scientific computing problems. We find that NA-QBC achieves better average performance than random sampling on every benchmark problem, while QBC can be detrimental if the wrong hyperparameters are chosen.

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