mlsanityData to 14 Oct 2026

Reliable Neural Networks for Regression Uncertainty Estimation

arXiv 2109.08213 ↗First posted 16 Sep 2021cs.LG stat.MLcited by 6

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Abstract (arXiv)

While deep neural networks are highly performant and successful in a wide range of real-world problems, estimating their predictive uncertainty remains a challenging task. To address this challenge, we propose and implement a loss function for regression uncertainty estimation based on the Bayesian Validation Metric (BVM) framework while using ensemble learning. The proposed loss reproduces maximum likelihood estimation in the limiting case. A series of experiments on in-distribution data show that the proposed method is competitive with existing state-of-the-art methods. Experiments on out-of-distribution data show that the proposed method is robust to statistical change and exhibits superior predictive capability.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 17 of its 46 references that are in the map. First posted on arXiv 16 Sep 2021.

Builds on

17 of 46 references.

References · 17

  1. 2017On Calibration of Modern Neural Networkscited by 4,960
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
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  6. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  7. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  8. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  9. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
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  11. 2014Deep Speech: Scaling up end-to-end speech recognitioncited by 354
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5 more
  1. 2020Bayesian Deep Ensembles via the Neural Tangent Kernelcited by 90
  2. 2019Evaluating and Calibrating Uncertainty Prediction in Regression Taskscited by 87
  3. 2021Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imagingcited by 18
  4. 2013Expectation Propagation for Neural Networks with Sparsity-promoting Priorscited by 7
  5. 2019A General Model Validation and Testing Toolcited by 6

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Cited by year

Citing papers per year: 2023: 4; 2024: 1; 2025: 1.

6 papers.

  1. 2023Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivitycited by 13
  2. 2023MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimationcited by 6
  3. 2023Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Predictioncited by 6
  4. 2023B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterizationcited by 3
  5. 2024ISR: Invertible Symbolic Regressioncited by 2
  6. 2025Generalization is not a universal guarantee: Estimating similarity to training data with an ensemble out-of-distribution metriccited by 0