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Multivariate Bayesian Last Layer for Regression with Uncertainty Quantification and Decomposition

arXiv 2405.01761 ↗First posted 2 May 2024stat.ML cs.LGcited by 0

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Abstract (arXiv)

We present new Bayesian Last Layer neural network models in the setting of multivariate regression under heteroscedastic noise, and propose EM algorithms for parameter learning. Bayesian modeling of a neural network's final layer has the attractive property of uncertainty quantification with a single forward pass. The proposed framework is capable of disentangling the aleatoric and epistemic uncertainty, and can be used to enhance a canonically trained deep neural network with uncertainty-aware capabilities.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 43 references that are in the map. First posted on arXiv 2 May 2024.

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References · 17

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  4. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 560
  5. 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
  6. 2015Deep Kernel Learningcited by 445
  7. 2019Deep Evidential Regressioncited by 355
  8. 2015Scalable Bayesian Optimization Using Deep Neural Networkscited by 284
  9. 2020Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networkscited by 231
  10. 2024Variational Bayesian Last Layerscited by 69
  11. 2022Do Bayesian Neural Networks Need To Be Fully Stochastic?cited by 63
  12. 2014Student-t Processes as Alternatives to Gaussian Processescited by 45
5 more
  1. 2022The Unreasonable Effectiveness of Deep Evidential Regressioncited by 40
  2. 2019Benchmarking the Neural Linear Model for Regressioncited by 31
  3. 2021Multivariate Deep Evidential Regressioncited by 23
  4. 2023Improved uncertainty quantification for neural networks with Bayesian last layercited by 12
  5. 2023Sparse Variational Student-t Processescited by 8

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