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Uncertainty Quantification using Generative Approach

arXiv 2310.09338 ↗First posted 13 Oct 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

We present the Incremental Generative Monte Carlo (IGMC) method, designed to measure uncertainty in deep neural networks using deep generative approaches. IGMC iteratively trains generative models, adding their output to the dataset, to compute the posterior distribution of the expectation of a random variable. We provide a theoretical guarantee of the convergence rate of IGMC relative to the sample size and sampling depth. Due to its compatibility with deep generative approaches, IGMC is adaptable to both neural network classification and regression tasks. We empirically study the behavior of IGMC on the MNIST digit classification task.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 30 references that are in the map. First posted on arXiv 13 Oct 2023.

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References · 24

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12 more
  1. 2017EMNIST: an extension of MNIST to handwritten letterscited by 213
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  3. 2019Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Drivingcited by 105
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  7. 2019Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCTcited by 42
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  11. 2021Bridging the Gap Between Explainable AI and Uncertainty Quantification to Enhance Trustabilitycited by 11
  12. 2020Uncertainty-Aware Few-Shot Image Classificationcited by 9

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