mlsanityData to 7 Oct 2026

Variational Neural Networks

arXiv 2207.01524 ↗First posted 4 Jul 2022cs.LG stat.MLcited by 7

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Abstract (arXiv)

Bayesian Neural Networks (BNNs) provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each input. In this paper, we propose a method for uncertainty estimation in neural networks which, instead of considering a distribution over weights, samples outputs of each layer from a corresponding Gaussian distribution, parametrized by the predictions of mean and variance sub-layers. In uncertainty quality estimation experiments, we show that the proposed method achieves better uncertainty quality than other single-bin Bayesian Model Averaging methods, such as Monte Carlo Dropout or Bayes By Backpropagation methods.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 13 of its 29 references that are in the map. First posted on arXiv 4 Jul 2022.

Builds on

13 of 29 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2013Auto-Encoding Variational Bayescited by 3,980
  4. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  5. 2016Variational Inference: A Review for Statisticianscited by 1,466
  6. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  7. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  8. 2017Deep Neural Networks as Gaussian Processescited by 617
  9. 2015Weight Uncertainty in Neural Networkscited by 554
  10. 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
  11. 2018Randomized Prior Functions for Deep Reinforcement Learningcited by 218
  12. 2021Epistemic Neural Networkscited by 120
1 more
  1. 2020Hypermodels for Explorationcited by 37

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Cited by year

Citing papers per year: 2022: 2; 2023: 3; 2024: 1; 2025: 1.

7 papers.

  1. 2023Quantification of Uncertainty with Adversarial Modelscited by 28
  2. 2022Bayesian Learning for Neural Networks: an algorithmic surveycited by 22
  3. 2022Layer Ensemblescited by 3
  4. 2023Uncertainty-Aware AB3DMOT by Variational 3D Object Detectioncited by 3
  5. 2024Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learningcited by 2
  6. 2023Variational Voxel Pseudo Image Trackingcited by 0
  7. 2025Variational Graph Convolutional Neural Networkscited by 0