Methods › General › Regularization › Concrete Dropout
Concrete Dropout
Introduced by Yarin Gal et al. in Concrete Dropout
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The archive carries only a placeholder description for this method.
Papers archive 2025-07-28
14 shown of 14, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Simplified Concrete Dropout -- Improving the Generation of Attribution Masks for Fine-grained Classification 27 Jul 2023 · 0 repositories · arXiv:2307.14825
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Fully Bayesian VIB-DeepSSM 9 May 2023 · 1 repository · arXiv:2305.05797Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)
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Optimal Training of Mean Variance Estimation Neural Networks 17 Feb 2023 · 1 repository · arXiv:2302.08875
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Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control Tasks 25 Oct 2021 · 1 repository · arXiv:2110.13079
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Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks 27 Sep 2021 · 0 repositories · arXiv:2109.12869
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$μ$DARTS: Model Uncertainty-Aware Differentiable Architecture Search 24 Jul 2021 · 0 repositories · arXiv:2107.11500
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Calibration and Uncertainty Quantification of Bayesian Convolutional Neural Networks for Geophysical Applications 25 May 2021 · 0 repositories · arXiv:2105.12115
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Generating Diverse Translation from Model Distribution with Dropout 16 Oct 2020 · 0 repositories · arXiv:2010.08178
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A Deep Neural Network Tool for Automatic Segmentation of Human Body Parts in Natural Scenes 8 Sep 2020 · 1 repository · arXiv:2009.09900
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Bayesian Conditional GAN for MRI Brain Image Synthesis 25 May 2020 · 0 repositories · arXiv:2005.11875
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Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms 22 Apr 2020 · 1 repository · arXiv:2004.10710Syntology ran 0 of 6 samples · 6 unverified
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Deep Reinforcement Learning with Weighted Q-Learning 20 Mar 2020 · 0 repositories · arXiv:2003.09280
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Analysing Dropout and Compounding Errors in Neural Language Models 2 Nov 2018 · 0 repositories · arXiv:1811.00998
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Concrete Dropout 22 May 2017 · 5 repositories · arXiv:1705.07832Syntology ran 4 of 4 samples · 0 unverified
Tasks archive 2025-07-28
20 shown of 29 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Reinforcement Learning (RL) | 3 |
| Uncertainty Quantification | 3 |
| Variational Inference | 3 |
| Gaussian Processes | 2 |
| Reinforcement Learning | 2 |
| Translation | 2 |
| model | 2 |
| Anatomy | 1 |
| Bayesian Inference | 1 |
| Benchmarking | 1 |
| Classification | 1 |
| Continuous Control | 1 |
| Decoder | 1 |
| Deep Reinforcement Learning | 1 |
| Denoising | 1 |
| Diversity | 1 |
| Domain Adaptation | 1 |
| Fault Detection | 1 |
| Generative Adversarial Network | 1 |
| Image Generation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections