Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations | Paper Citation Record

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This paper cites Explainable machine learning in deployment.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Explainable machine learning in deployment

Reference 1

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This paper cites Reliability, sufficiency, and the decomposition of proper scores.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Reliability, sufficiency, and the decomposition of proper scores

Reference 2

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This paper cites Overinterpretation reveals image classification model pathologies.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Overinterpretation reveals image classification model pathologies

Reference 3

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This paper cites Learning to explain: An information-theoretic perspective on model interpretation.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Learning to explain: An information-theoretic perspective on model interpretation

Reference 4

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This paper cites Understanding global feature contributions with additive importance measures.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Understanding global feature contributions with additive importance measures

Reference 5

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This paper cites Explaining by removing: A unified framework for model explanation.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Explaining by removing: A unified framework for model explanation

Reference 6

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This paper cites Does your model think like an engineer? explainable ai for bearing fault detection with deep learning.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Does your model think like an engineer? explainable ai for bearing fault detection with deep learning

Reference 7

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This paper cites Explanatory model monitoring to understand the effects of feature shifts on performance.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Explanatory model monitoring to understand the effects of feature shifts on performance

Reference 8

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

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This paper cites Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis

Reference 10

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This paper cites Harmonizing the object recognition strategies of deep neural networks with humans.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Harmonizing the object recognition strategies of deep neural networks with humans

Reference 11

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This paper cites Pathologies of neural models make interpretations difficult.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Pathologies of neural models make interpretations difficult

Reference 12

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This paper cites Shapley explainability on the data manifold.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Shapley explainability on the data manifold

Reference 13

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This paper cites Strictly proper scoring rules, prediction, and estimation.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Strictly proper scoring rules, prediction, and estimation

Reference 14

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This paper cites Better uncertainty calibration via proper scores for classification and beyond.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Better uncertainty calibration via proper scores for classification and beyond

Reference 15

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This paper cites On calibration of modern neural networks.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations On calibration of modern neural networks

Reference 16

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This paper cites The out-of-distribution problem in explainability and search methods for feature importance explanations.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations The out-of-distribution problem in explainability and search methods for feature importance explanations

Reference 17

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This paper cites o m, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina M-C H \.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations o m, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina M-C H \

Reference 18

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This paper cites Densely connected convolutional networks.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Densely connected convolutional networks

Reference 19

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This paper cites Openclip, July 2021.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Openclip, July 2021

Reference 20

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This paper cites Perturbation-based methods for explaining deep neural networks: A survey.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Perturbation-based methods for explaining deep neural networks: A survey

Reference 21

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This paper cites Missingness bias in model debugging.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Missingness bias in model debugging

Reference 22

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This paper cites Trustworthy artificial intelligence: a review.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Trustworthy artificial intelligence: a review

Reference 23

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This paper cites Captum: A unified and generic model interpretability library for PyTorch.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Captum: A unified and generic model interpretability library for PyTorch

Reference 24

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This paper cites Novel decompositions of proper scoring rules for classification: Score adjustment as precursor to calibration.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Novel decompositions of proper scoring rules for classification: Score adjustment as precursor to calibration

Reference 25

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This paper cites On the robustness of removal-based feature attributions.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations On the robustness of removal-based feature attributions

Reference 26

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This paper cites Learning what and where to attend.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Learning what and where to attend

Reference 27

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This paper cites On the limited memory bfgs method for large scale optimization.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations On the limited memory bfgs method for large scale optimization

Reference 28

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This paper cites o fstr \.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations o fstr \

Reference 29

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This paper cites A unified approach to interpreting model predictions.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations A unified approach to interpreting model predictions

Reference 30

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This paper cites From local explanations to global understanding with explainable ai for trees.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations From local explanations to global understanding with explainable ai for trees

Reference 31

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This paper cites Local calibration: metrics and recalibration.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Local calibration: metrics and recalibration

Reference 32

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This paper cites Revisiting the calibration of modern neural networks.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Revisiting the calibration of modern neural networks

Reference 33

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This paper cites Obtaining well calibrated probabilities using bayesian binning.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Obtaining well calibrated probabilities using bayesian binning

Reference 34

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This paper cites Predicting good probabilities with supervised learning.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Predicting good probabilities with supervised learning

Reference 35

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This paper cites Making sense of dependence: Efficient black-box explanations using dependence measure.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Making sense of dependence: Efficient black-box explanations using dependence measure

Reference 36

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This paper cites Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift

Reference 37

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This paper cites RISE: Randomized Input Sampling for Explanation of Black-box Models.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations RISE: Randomized Input Sampling for Explanation of Black-box Models

Reference 38

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This paper cites Consistent and asymptotically unbiased estimation of proper calibration errors.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Consistent and asymptotically unbiased estimation of proper calibration errors

Reference 39

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This paper cites why should i trust you?.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations why should i trust you?

Reference 40

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This paper cites Perturbation-based explanations of prediction models.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Perturbation-based explanations of prediction models

Reference 41

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This paper cites Calibrate to interpret.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Calibrate to interpret

Reference 42

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This paper cites Restricting the Flow: Information Bottlenecks for Attribution.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Restricting the Flow: Information Bottlenecks for Attribution

Reference 43

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This paper cites Classifier calibration: a survey on how to assess and improve predicted class probabilities.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Classifier calibration: a survey on how to assess and improve predicted class probabilities

Reference 44

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This paper cites Visualizing the impact of feature attribution baselines.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Visualizing the impact of feature attribution baselines

Reference 45

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This paper cites The many shapley values for model explanation.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations The many shapley values for model explanation

Reference 46

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This paper cites Post-hoc uncertainty calibration for domain drift scenarios.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Post-hoc uncertainty calibration for domain drift scenarios

Reference 47

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This paper cites Calibration in Deep Learning: A Survey of the State-of-the-Art.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Calibration in Deep Learning: A Survey of the State-of-the-Art

Reference 48

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This paper cites Pytorch image models.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Pytorch image models

Reference 49

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This paper cites Robust calibration with multi-domain temperature scaling.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Robust calibration with multi-domain temperature scaling

Reference 50

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This paper cites Visualizing and understanding convolutional networks.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Visualizing and understanding convolutional networks

Reference 51

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This paper cites Sigmoid loss for language image pre-training.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Sigmoid loss for language image pre-training

Reference 52

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This paper cites Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:13.590110Z

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Observation 21d90738-8972-4aef-b9d3-f9e617c862e5 · outbound

This paper cites Individual calibration with randomized forecasting.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Individual calibration with randomized forecasting

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:13.583118Z

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Observation 8acbcdc7-968b-439b-9078-73671b21b829 · outbound

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Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations @esa (Ref

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:13.511998Z

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Observation 17003933-3322-442d-a735-9907dbf17b96 · outbound

This paper cites an unresolved cited work.

Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations Unresolved cited work

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:13.514880Z

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Observation d7bd252c-98a4-4595-8186-09a8c5348fc6 · outbound

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Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations P;!־ ֪PTRޖP ՖQJ TIK ĒD Id_52d uz<s<ys B

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:12:13.567975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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