mlsanityData to 7 Oct 2026

Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements

arXiv 2103.01259 ↗First posted 1 Mar 2021cs.LG eess.IVcited by 11

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Abstract (arXiv)

Uncertainty quantification by ensemble learning is explored in terms of an application from computational optical form measurements. The application requires to solve a large-scale, nonlinear inverse problem. Ensemble learning is used to extend a recently developed deep learning approach for this application in order to provide an uncertainty quantification of its predicted solution to the inverse problem. By systematically inserting out-of-distribution errors as well as noisy data the reliability of the developed uncertainty quantification is explored. Results are encouraging and the proposed application exemplifies the ability of ensemble methods to make trustworthy predictions on high dimensional data in a real-world application.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 27 of its 55 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

27 of 55 references.

References · 27

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15 more
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Cited by year

Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 3; 2025: 2.

11 papers.

  1. 2021Deep Ensembles from a Bayesian Perspectivecited by 27
  2. 2022Uncertainty Quantification for Deep Unrolling-Based Computational Imagingcited by 10
  3. 2021An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictionscited by 6
  4. 2024BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averagingcited by 5
  5. 2021Aleatoric uncertainty for Errors-in-Variables models in deep regressioncited by 4
  6. 2023Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problemscited by 4
  7. 2025Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imagingcited by 4
  8. 2024Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Datacited by 3
  9. 2023Toward Robust Uncertainty Estimation with Random Activation Functionscited by 1
  10. 2025Flow-Induced Diagonal Gaussian Processescited by 1
  11. 2024Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Datacited by 0