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A framework for benchmarking uncertainty in deep regression
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DOI：10.1007/s10489-022-03908-3.png)
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We propose a framework for the assessment of uncertainty quantification in deep regression. The framework is based on regression problems where the regression function is a linear combination of nonlinear functions. Basically, any level of complexity can be realized through the choice of the nonlinear functions and the dimensionality of their domain. Results of an uncertainty quantification for deep regression are compared against those obtained by a statistical reference method. The reference method utilizes knowledge about the underlying nonlinear functions and is based on Bayesian linear regression using a prior reference. The flexibility, together with the availability of a reference solution, makes the framework suitable for defining benchmark sets for uncertainty quantification. Reliability of uncertainty quantification is assessed in terms of coverage probabilities, and accuracy through the size of calculated uncertainties. We illustrate the proposed framework by applying it to current approaches for uncertainty quantification in deep regression. In addition, results for three real-world regression tasks are presented.
Keyword:
Deep learning
Bayesian neural networks
Deep regression
Reference prior
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
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引用论文
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons科学机器学习中的不确定性量化: 方法、度量和比较
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