Qualitative Analysis of Monte Carlo Dropout
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Abstract (arXiv)
In this report, we present qualitative analysis of Monte Carlo (MC) dropout method for measuring model uncertainty in neural network (NN) models. We first consider the sources of uncertainty in NNs, and briefly review Bayesian Neural Networks (BNN), the group of Bayesian approaches to tackle uncertainties in NNs. After presenting mathematical formulation of MC dropout, we proceed to suggesting potential benefits and associated costs for using MC dropout in typical NN models, with the results from our experiments.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 0; 2024: 4; 2025: 1; 2026: 1.
11 papers.
- 2022AI Security for Geoscience and Remote Sensing: Challenges and Future Trendscited by 23
- 2025Active operator learning with predictive uncertainty quantification for partial differential equationscited by 12
- 2021Confidence Aware Neural Networks for Skin Cancer Detectioncited by 5
- 2022Controlled Dropout for Uncertainty Estimationcited by 4
- 2024The Probabilistic Tsetlin Machine: A Novel Approach to Uncertainty Quantificationcited by 2
- 2022Embedded Ensembles: Infinite Width Limit and Operating Regimescited by 1
- 2022MAViC: Multimodal Active Learning for Video Captioningcited by 1
- 2024Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networkscited by 1
- 2024Divergent Ensemble Networks: Enhancing Uncertainty Estimation with Shared Representations and Independent Branchingcited by 0
- 2024Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention for Fine-Grained Few-Shot Learningcited by 0
- 2026Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learningcited by 0
- 2026Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learning14 Apr 2026
- 2025Active operator learning with predictive uncertainty quantification for partial differential equations5 Mar 2025
- 2024Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention for Fine-Grained Few-Shot Learning19 Dec 2024
- 2024Divergent Ensemble Networks: Enhancing Uncertainty Estimation with Shared Representations and Independent Branching2 Dec 2024
- 2024The Probabilistic Tsetlin Machine: A Novel Approach to Uncertainty Quantification23 Oct 2024
- 2024Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks27 Jun 2024
- 2022AI Security for Geoscience and Remote Sensing: Challenges and Future Trends19 Dec 2022
- 2022MAViC: Multimodal Active Learning for Video Captioning11 Dec 2022
- 2022Controlled Dropout for Uncertainty Estimation6 May 2022
- 2022Embedded Ensembles: Infinite Width Limit and Operating Regimes24 Feb 2022
- 2021Confidence Aware Neural Networks for Skin Cancer Detection19 Jul 2021