Reference health

Dynamic Robustness Evaluation for Automated Model Selection in Operation

https://doi.org/10.2139/ssrn.4571365
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9/9 checkable references clean · checked 2026-08-28

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

45 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 9 checked references that resolve
resolves10.1109/ACCESS.2020.3010274
Anomalous Example Detection in Deep Learning: A Survey
resolves10.1007/s10115-018-1257-z
Survey of distance measures for quantifying concept drift and shift in numeric data
resolves10.1023/A:1026543900054
The Earth Mover's Distance as a Metric for Image Retrieval
resolves10.1007/978-1-4612-5931-2_7
Kolmogorov-Smirnov Two-Sample Tests
resolves10.1007/s10618-011-0222-1
Hellinger distance decision trees are robust and skew-insensitive
resolves10.1007/978-3-642-04898-2_327
Kullback-Leibler Divergence
resolves10.1145/3417330
Test Selection for Deep Learning Systems
resolves10.1177/001316445401400215
Procedures for the Analysis of Classroom Tests
resolves10.1016/j.neunet.2017.09.003
Robust artificial neural network for reliability and sensitivity analyses of complex non-linear systems
The 45 references without a DOI — listed, not checked
no DOI — not checkedThe many faces of robustness: A critical analysis of out-of-distribution generalization
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no DOI — not checkedA winning hand: Compressing deep networks can improve out-of-distribution robustness
no DOI — not checkedArtificial neural networks: An overview
no DOI — not checkedQuantifying dnn model robustness to the realworld threats
no DOI — not checkedref9
no DOI — not checkedLearning to validate the predictions of black box classifiers on unseen data
no DOI — not checkedAre labels always necessary for classifier accuracy evaluation?
no DOI — not checkedFailing loudly: An empirical study of methods for detecting dataset shift
no DOI — not checkedAugur: A step towards realistic drift detection in production ml systems
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no DOI — not checked3d common corruptions and data augmentation
no DOI — not checkedMeasuring discrimination to boost comparative testing for multiple deep learning models
no DOI — not checkedEnsemble learning: A survey
no DOI — not checkedSelf-checking deep neural networks in deployment
no DOI — not checkedSoftware engineering for machine learning: A case study
no DOI — not checkedComprehensive survey on distance/similarity measures between probability density functions
no DOI — not checkedref29
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no DOI — not checkedAdversarial examples are not bugs, they are features
no DOI — not checkedRobust design of deep neural networks against adversarial attacks based on lyapunov theory
no DOI — not checkedref33
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no DOI — not checkedBetter diffusion models further improve adversarial training
no DOI — not checkedref38
no DOI — not checkedPrime: A few primitives can boost robustness to common corruptions
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no DOI — not checkedAdversarial robustness: From self-supervised pre-training to fine-tuning
no DOI — not checkedQuality metrics in recommender systems: Do we calculate metrics consistently?
no DOI — not checkedref46
no DOI — not checkedUnlabeled data improves adversarial robustness
no DOI — not checkedref49
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no DOI — not checkedExploring neural networks with activation atlases
no DOI — not checkedref53
no DOI — not checkedref54
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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