mlsanityData to 7 Oct 2026

Estimating Model Uncertainty of Neural Networks in Sparse Information Form

arXiv 2006.11631 ↗First posted 20 Jun 2020cs.LG cs.CV cs.ROcited by 31

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Abstract (arXiv)

We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs) where the parameter posterior is approximated with an inverse formulation of the Multivariate Normal Distribution (MND), also known as the information form. The key insight of our work is that the information matrix, i.e. the inverse of the covariance matrix tends to be sparse in its spectrum. Therefore, dimensionality reduction techniques such as low rank approximations (LRA) can be effectively exploited. To achieve this, we develop a novel sparsification algorithm and derive a cost-effective analytical sampler. As a result, we show that the information form can be scalably applied to represent model uncertainty in DNNs. Our exhaustive theoretical analysis and empirical evaluations on various benchmarks show the competitiveness of our approach over the current methods.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2020.

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Citing papers per year: 2021: 8; 2022: 4; 2023: 7; 2024: 7; 2025: 2; 2026: 3.

31 papers.

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  1. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  2. 2021Laplace Redux -- Effortless Bayesian Deep Learningcited by 359
  3. 2024LUQ: Long-text Uncertainty Quantification for LLMscited by 79
  4. 2021On the Practicality of Deterministic Epistemic Uncertaintycited by 62
  5. 2021NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weightscited by 28
  6. 2024A Comprehensive Survey on Evidential Deep Learning and Its Applicationscited by 25
  7. 2021A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applicationscited by 24
  8. 2023FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregationcited by 22
  9. 2021Explaining Bayesian Neural Networkscited by 18
  10. 2024Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Plannerscited by 15
  11. 2021Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processescited by 12
  12. 2023Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimationcited by 11
19 more
  1. 2022Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picturecited by 10
  2. 2024Epistemic Uncertainty Quantification For Pre-trained Neural Networkcited by 10
  3. 2021Bayesian Active Learning for Sim-to-Real Robotic Perceptioncited by 8
  4. 2023Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Searchcited by 8
  5. 2023Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learningcited by 7
  6. 2024Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classificationcited by 6
  7. 2023Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networkscited by 4
  8. 2025Martingale Posterior Neural Networks for Fast Sequential Decision Makingcited by 4
  9. 2022TEDL: A Two-stage Evidential Deep Learning Method for Classification Uncertainty Quantificationcited by 3
  10. 2023Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assemblycited by 2
  11. 2025Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networkscited by 2
  12. 2022Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networkscited by 1
  13. 2022Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilitiescited by 1
  14. 2023Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximationscited by 0
  15. 2024Addressing the Inconsistency in Bayesian Deep Learning via Generalized Laplace Approximationcited by 0
  16. 2024Stein Variational Newton Neural Network Ensemblescited by 0
  17. 2026SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertaintycited by 0
  18. 2026Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantificationcited by 0
  19. 2026Instance-Level Post Hoc Uncertainty Quantification in Object Detectioncited by 0