mlsanityData to 7 Oct 2026

Being Bayesian about Categorical Probability

arXiv 2002.07965 ↗First posted 19 Feb 2020cs.LG stat.MLcited by 35

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Abstract (arXiv)

Neural networks utilize the softmax as a building block in classification tasks, which contains an overconfidence problem and lacks an uncertainty representation ability. As a Bayesian alternative to the softmax, we consider a random variable of a categorical probability over class labels. In this framework, the prior distribution explicitly models the presumed noise inherent in the observed label, which provides consistent gains in generalization performance in multiple challenging tasks. The proposed method inherits advantages of Bayesian approaches that achieve better uncertainty estimation and model calibration. Our method can be implemented as a plug-and-play loss function with negligible computational overhead compared to the softmax with the cross-entropy loss function.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Feb 2020.

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Citing papers per year: 2021: 6; 2022: 10; 2023: 8; 2024: 6; 2025: 3; 2026: 2.

35 papers.

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  1. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  2. 2021Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimationcited by 71
  3. 2022Better Uncertainty Calibration via Proper Scores for Classification and Beyondcited by 65
  4. 2022Sample-dependent Adaptive Temperature Scaling for Improved Calibrationcited by 48
  5. 2022Post-hoc Uncertainty Learning using a Dirichlet Meta-Modelcited by 31
  6. 2022Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcitycited by 24
  7. 2023Dirichlet-based Uncertainty Calibration for Active Domain Adaptationcited by 23
  8. 2024Uncertainty Estimation by Density Aware Evidential Deep Learningcited by 22
  9. 2021Robust Semantic Segmentation with Superpixel-Mixcited by 19
  10. 2024Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?cited by 17
  11. 2023EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoderscited by 16
  12. 2022Ambiguous Images With Human Judgments for Robust Visual Event Classificationcited by 12
23 more
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  2. 2023Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptationcited by 11
  3. 2023Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errorscited by 10
  4. 2022Segmenting Known Objects and Unseen Unknowns without Prior Knowledgecited by 9
  5. 2023Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regressioncited by 7
  6. 2022Revisiting Softmax for Uncertainty Approximation in Text Classificationcited by 4
  7. 2024Optimizing Estimators of Squared Calibration Errors in Classificationcited by 3
  8. 2021Fuzzy Classification of Multi-intent Utterancescited by 2
  9. 2023A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correctioncited by 2
  10. 2025Enhancing Uncertainty Estimation and Interpretability via Bayesian Non-negative Decision Layercited by 2
  11. 2026Evidential Transformation Network: Turning Pretrained Models into Evidential Models for Post-hoc Uncertainty Estimationcited by 2
  12. 2022Switchable Representation Learning Framework with Self-compatibilitycited by 1
  13. 2022Bayesian Evidential Learning for Few-Shot Classificationcited by 1
  14. 2023IW-GAE: Importance Weighted Group Accuracy Estimation for Improved Calibration and Model Selection in Unsupervised Domain Adaptationcited by 1
  15. 2024Improving self-training under distribution shifts via anchored confidence with theoretical guaranteescited by 1
  16. 2025Subjective Logic Encodingscited by 1
  17. 2025MedBayes-Lite: A Clinical Uncertainty Governance Layer for Risk-Aware Medical Decision Supportcited by 1
  18. 2021Scene Uncertainty and the Wellington Posterior of Deterministic Image Classifierscited by 0
  19. 2021Disturbing Target Values for Neural Network Regularizationcited by 0
  20. 2023Function-Space Regularization for Deep Bayesian Classificationcited by 0
  21. 2024Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learningcited by 0
  22. 2024Evidential Transformers for Improved Image Retrievalcited by 0
  23. 2026Bayesian Preference Learning for Test-Time Steerable Reward Modelscited by 0