mlsanityData to 14 Oct 2026

Notes on the Behavior of MC Dropout

arXiv 2008.02627 ↗First posted 6 Aug 2020cs.LG stat.MLcited by 23

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Abstract (arXiv)

Among the various options to estimate uncertainty in deep neural networks, Monte-Carlo dropout is widely popular for its simplicity and effectiveness. However the quality of the uncertainty estimated through this method varies and choices in architecture design and in training procedures have to be carefully considered and tested to obtain satisfactory results. In this paper we present a study offering a different point of view on the behavior of Monte-Carlo dropout, which enables us to observe a few interesting properties of the technique to keep in mind when considering its use for uncertainty estimation.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Aug 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 2; 2024: 5; 2025: 8; 2026: 2.

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  1. 2021Uncertainty-Aware Machine Translation Evaluationcited by 35
  2. 2021Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Drivingcited by 10
  3. 2025Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approachcited by 6
  4. 2022What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorizationcited by 5
  5. 2024Bayesian neural networks for predicting uncertainty in full-field material responsecited by 5
  6. 2023B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterizationcited by 3
  7. 2024Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Datacited by 3
  8. 2024Thermal Earth Model for the Conterminous United States Using an Interpolative Physics-Informed Graph Neural Network (InterPIGNN)cited by 2
  9. 2024Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantificationcited by 2
  10. 2025Unreliable Uncertainty Estimates with Monte Carlo Dropoutcited by 2
  11. 2021RBUE: A ReLU-Based Uncertainty Estimation Method of Deep Neural Networkscited by 1
  12. 2022Embedded Ensembles: Infinite Width Limit and Operating Regimescited by 1
11 more
  1. 2022Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothingcited by 1
  2. 2024Uncertainty Quantification for Deep Learningcited by 1
  3. 2025Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Reviewcited by 1
  4. 2025A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysiscited by 1
  5. 2025Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inferencecited by 1
  6. 2023Amortised Inference in Bayesian Neural Networkscited by 0
  7. 2025Artificial Behavior Intelligence: Technology, Challenges, and Future Directionscited by 0
  8. 2025Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Modelscited by 0
  9. 2025Convergence, design and training of continuous-time dropout as a random batch methodcited by 0
  10. 2026A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learningcited by 0
  11. 2026Rethinking Epistemic Uncertainty in Node Classification through Information Growthcited by 0