Uncertainty Quantification using Generative Approach

• URL: http://arxiv.org/abs/2310.09338v1
• Date: Fri, 13 Oct 2023 18:05:25 GMT
• Title: Uncertainty Quantification using Generative Approach
• Authors: Yunsheng Zhang
• Abstract summary: We present the Incremental Generative Monte Carlo (IGMC) method to measure uncertainty in deep neural networks. IGMC iteratively trains generative models, adding their output to the dataset, to compute the posterior distribution of the expectation of a random variable. We empirically study the behavior of IGMC on the MNIST digit classification task.
• Score: 4.4858968464373845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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