Dropout as a Bayesian Approximation: Appendix
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Abstract (arXiv)
We show that a neural network with arbitrary depth and non-linearities, with dropout applied before every weight layer, is mathematically equivalent to an approximation to a well known Bayesian model. This interpretation might offer an explanation to some of dropout's key properties, such as its robustness to over-fitting. Our interpretation allows us to reason about uncertainty in deep learning, and allows the introduction of the Bayesian machinery into existing deep learning frameworks in a principled way. This document is an appendix for the main paper "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning" by Gal and Ghahramani, 2015.
Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jun 2015.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 3; 2024: 1; 2025: 2; 2026: 1.
14 papers.
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- 2021LiBRe: A Practical Bayesian Approach to Adversarial Detectioncited by 40
- 2022Out of Distribution Data Detection Using Dropout Bayesian Neural Networkscited by 15
- 2023That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentationcited by 13
- 2021MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learningcited by 5
- 2022Revisiting Softmax for Uncertainty Approximation in Text Classificationcited by 4
- 2025Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurementscited by 4
- 2024Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severitycited by 3
- 2021Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimationcited by 2
- 2021Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Datacited by 1
- 2022Self-Normalized Density Map (SNDM) for Counting Microbiological Objectscited by 1
- 2023Deep learning for diffusion in porous mediacited by 1
- 2023Defensive Perception: Estimation and Monitoring of Neural Network Performance under Deploymentcited by 0
- 2026Linear Fitness Subspace in Protein Language Models Enables Sample-Efficient Directed Evolution6 Oct 2026
- 2025Explainable Image Classification with Reduced Overconfidence for Tissue Characterisation31 Jul 2025
- 2025Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurements6 Jan 2025
- 2024Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity8 Sep 2024
- 2023Defensive Perception: Estimation and Monitoring of Neural Network Performance under Deployment11 Aug 2023
- 2023Deep learning for diffusion in porous media4 Apr 2023
- 2023That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation28 Mar 2023
- 2022Revisiting Softmax for Uncertainty Approximation in Text Classification25 Oct 2022
- 2022Self-Normalized Density Map (SNDM) for Counting Microbiological Objects15 Mar 2022
- 2022Out of Distribution Data Detection Using Dropout Bayesian Neural Networks18 Feb 2022
- 2021Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data15 Dec 2021
- 2021MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning22 Sep 2021
2 more
- 2021LiBRe: A Practical Bayesian Approach to Adversarial Detectioncited by 40
- 2022Out of Distribution Data Detection Using Dropout Bayesian Neural Networkscited by 15
- 2023That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentationcited by 13
- 2021MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learningcited by 5
- 2022Revisiting Softmax for Uncertainty Approximation in Text Classificationcited by 4
- 2025Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurementscited by 4
- 2024Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severitycited by 3
- 2021Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimationcited by 2
- 2021Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Datacited by 1
- 2022Self-Normalized Density Map (SNDM) for Counting Microbiological Objectscited by 1
- 2023Deep learning for diffusion in porous mediacited by 1
- 2023Defensive Perception: Estimation and Monitoring of Neural Network Performance under Deploymentcited by 0
12 most cited of 14 All 14 (JSON)
- 2026Linear Fitness Subspace in Protein Language Models Enables Sample-Efficient Directed Evolution6 Oct 2026
- 2025Explainable Image Classification with Reduced Overconfidence for Tissue Characterisation31 Jul 2025
- 2025Low-Order Flow Reconstruction and Uncertainty Quantification in Disturbed Aerodynamics Using Sparse Pressure Measurements6 Jan 2025
- 2024Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity8 Sep 2024
- 2023Defensive Perception: Estimation and Monitoring of Neural Network Performance under Deployment11 Aug 2023
- 2023Deep learning for diffusion in porous media4 Apr 2023
- 2023That Label's Got Style: Handling Label Style Bias for Uncertain Image Segmentation28 Mar 2023
- 2022Revisiting Softmax for Uncertainty Approximation in Text Classification25 Oct 2022
- 2022Self-Normalized Density Map (SNDM) for Counting Microbiological Objects15 Mar 2022
- 2022Out of Distribution Data Detection Using Dropout Bayesian Neural Networks18 Feb 2022
- 2021Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data15 Dec 2021
- 2021MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning22 Sep 2021
12 newest of 14 All 14 (JSON)