mlsanityData to 7 Oct 2026

Deep Gaussian Mixture Ensembles

arXiv 2306.07235 ↗First posted 12 Jun 2023stat.ML cs.LG stat.MEcited by 4

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Abstract (arXiv)

This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty. By assuming the data generating process follows that of a Gaussian mixture, DGMEs are capable of approximating complex probability distributions, such as heavy-tailed or multimodal distributions. Our contributions include the derivation of an expectation-maximization (EM) algorithm used for learning the model parameters, which results in an upper-bound on the log-likelihood of training data over that of standard deep ensembles. Additionally, the proposed EM training procedure allows for learning of mixture weights, which is not commonly done in ensembles. Our experimental results demonstrate that DGMEs outperform state-of-the-art uncertainty quantifying deep learning models in handling complex predictive densities.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 48 references that are in the map. First posted on arXiv 12 Jun 2023.

Builds on

24 of 48 references.

References · 24

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  4. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  5. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
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12 more
  1. 2017Neural Expectation Maximizationcited by 123
  2. 2018Uncertainty in Neural Networks: Approximately Bayesian Ensemblingcited by 94
  3. 2022A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglementcited by 87
  4. 2018Bayesian Uncertainty Estimation for Batch Normalized Deep Networkscited by 70
  5. 2019Estimating Risk and Uncertainty in Deep Reinforcement Learningcited by 68
  6. 2018Deep Neural Networks for Estimation and Inferencecited by 62
  7. 2018Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Controlcited by 54
  8. 2020Fast Predictive Uncertainty for Classification with Bayesian Deep Networkscited by 31
  9. 2021Deep Ensembles from a Bayesian Perspectivecited by 27
  10. 2022Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learningcited by 27
  11. 2016Recurrent Mixture Density Network for Spatiotemporal Visual Attentioncited by 16
  12. 2020Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensemblescited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2023Augment on Manifold: Mixup Regularization with UMAPcited by 3
  2. 2025Mixup Regularization: A Probabilistic Perspectivecited by 1
  3. 2025Rethinking Nonlinearity: Trainable Gaussian Mixture Modules for Modern Neural Architecturescited by 0
  4. 2026Uncertainty Estimation via Hyperspherical Confidence Mappingcited by 0