mlsanityData to 7 Oct 2026

CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks

arXiv 2401.05043 ↗First posted 10 Jan 2024cs.LG cs.AIcited by 26

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Abstract (arXiv)

Effective uncertainty estimation is becoming increasingly attractive for enhancing the reliability of neural networks. This work presents a novel approach, termed Credal-Set Interval Neural Networks (CreINNs), for classification. CreINNs retain the fundamental structure of traditional Interval Neural Networks, capturing weight uncertainty through deterministic intervals. CreINNs are designed to predict an upper and a lower probability bound for each class, rather than a single probability value. The probability intervals can define a credal set, facilitating estimating different types of uncertainties associated with predictions. Experiments on standard multiclass and binary classification tasks demonstrate that the proposed CreINNs can achieve superior or comparable quality of uncertainty estimation compared to variational Bayesian Neural Networks (BNNs) and Deep Ensembles. Furthermore, CreINNs significantly reduce the computational complexity of variational BNNs during inference. Moreover, the effective uncertainty quantification of CreINNs is also verified when the input data are intervals.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 30 of its 76 references that are in the map. First posted on arXiv 10 Jan 2024.

Builds on

30 of 76 references.

References · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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18 more
  1. 2020Fairness without Demographics through Adversarially Reweighted Learningcited by 251
  2. 2019Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Visioncited by 182
  3. 2018Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batchescited by 133
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  12. 2023Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?cited by 40
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  14. 2022The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalizationcited by 21
  15. 2021Ensemble-based Uncertainty Quantification: Bayesian versus Credal Inferencecited by 16
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 8; 2026: 12.

26 papers.

Sort:

  1. 2024Credal Learning Theorycited by 30
  2. 2024Credal Wrapper of Model Averaging for Uncertainty Estimation in Classificationcited by 13
  3. 2025Integral Imprecise Probability Metricscited by 11
  4. 2024Scoring Rules and Calibration for Imprecise Probabilitiescited by 10
  5. 2025A Unified Evaluation Framework for Epistemic Predictionscited by 10
  6. 2025Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'cited by 8
  7. 2025A calibration test for evaluating set-based epistemic uncertainty representationscited by 7
  8. 2025Random-Set Large Language Modelscited by 7
  9. 2026Learning Credal Ensembles via Distributionally Robust Optimizationcited by 6
  10. 2024Generalising realisability in statistical learning theory under epistemic uncertaintycited by 5
  11. 2025Epistemic Wrapping for Uncertainty Quantificationcited by 5
  12. 2026Quantifying Epistemic Predictive Uncertainty in Conformal Predictioncited by 4
14 more
  1. 2026Direct Interval Propagation Methods using Neural-Network Surrogates for Uncertainty Quantification in Physical Systems Surrogate Modelcited by 3
  2. 2026Random-Set Graph Neural Networkscited by 3
  3. 2025Credal Graph Neural Networkscited by 2
  4. 2026Quantification of Credal Uncertainty: A Distance-Based Approachcited by 2
  5. 2026Credal Large Language Models for Semantic Commitment under Uncertaintycited by 2
  6. 2025When Do Credal Sets Stabilize? Fixed-Point Theorems for Credal Set Updatescited by 1
  7. 2026Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Studycited by 1
  8. 2026A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classificationcited by 1
  9. 2026A formal definition and meta-model for a machine theory of mindcited by 1
  10. 2024Generalisation of Total Uncertainty in AI: A Theoretical Studycited by 0
  11. 2024SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Losscited by 0
  12. 2026Efficient Credal Prediction through Decalibrationcited by 0
  13. 2026XU-RS: Explaining Credal Width in Random-Set Language Modelscited by 0
  14. 2026Understanding as No-Arbitrage: Bounded Dutch Books as a Definition and Training Objective for Language Modelscited by 0