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Paper Citation Record · LEDGER

AdaStop: Cost-Aware Early Stopping for DNN Test Selection

As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.05461.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.05461 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T16:56:48.568027Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

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External citation measurements

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Outbound references

Observation 15f99f21-a2a7-4d58-95f7-7c0298cda4dd · outbound

This paper cites Learning Multiple Layers of Features from Tiny Images,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Learning Multiple Layers of Features from Tiny Images,

Reference 1

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source=pdf_text observed=2026-07-11T16:56:48.568027Z digest=sha256:1e2634f67650c190ac759966232ed6b6709e6e1500c716cd4b8604e4fdcfc5fd

Observation bc4d2044-39ef-489d-8aa3-43149e71c19f · outbound

This paper cites Reading Digits in Natural Images with Unsuper- vised Feature Learning,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Reading Digits in Natural Images with Unsuper- vised Feature Learning,

Reference 2

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source=pdf_text observed=2026-07-11T16:56:48.568027Z digest=sha256:0c1de2c2ce3de28e1b6fd4285adcdd61f7315b8f2d2bc21c9cb5cc696f812ee1

Observation 5676780c-a276-4c18-a479-57ef00bc779b · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 3

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Observation 54812998-f812-4f11-b343-cf9694e3b0fc · outbound

This paper cites Deep Residual Learning for Image Recognition,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Deep Residual Learning for Image Recognition,

Reference 4

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Observation 60854c5d-e0a6-4d6e-acbb-9199794e59b1 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Very Deep Convolutional Networks for Large-Scale Image Recognition,

Reference 5

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Observation 75cfcbef-de84-4713-9e1f-32085ce98ca8 · outbound

This paper cites Densely Connected Convolutional Networks,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Densely Connected Convolutional Networks,

Reference 6

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source=pdf_text observed=2026-07-11T16:56:48.568027Z digest=sha256:13d8b44607bdafff44dad7d5524c045deda2f8daec04d29b1c0dbac7b8596b63

Observation 220c4acb-afaf-4040-855d-2ecfc1494a09 · outbound

This paper cites ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design,

Reference 7

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source=pdf_text observed=2026-07-11T16:56:48.568027Z digest=sha256:cf1974d4cb2fd8b3a456937c4e94a502c2e88b14e5d6de092a3fbf34af091928

Observation 884fe748-ab99-4e50-a90f-97119ae10a3b · outbound

This paper cites Probable Inference, the Law of Succession, and Statistical Inference,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Probable Inference, the Law of Succession, and Statistical Inference,

Reference 8

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source=pdf_text observed=2026-07-11T16:56:48.568027Z digest=sha256:8a8720a135c444ad10fa5fda49c1cd3d78862f480d7157469690202c6d7b3ea6

Observation 878cb58d-f32c-447e-a606-526df9c1c150 · outbound

This paper cites DeepGini: Prioritizing Massive Tests to Enhance the Robustness of Deep Neural Networks,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection DeepGini: Prioritizing Massive Tests to Enhance the Robustness of Deep Neural Networks,

Reference 9

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Observation 3e5e8d2e-9ee5-4377-98a6-7d9bcb0968f9 · outbound

This paper cites TestRank: Bringing order into unlabeled test instances for deep learning tasks,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection TestRank: Bringing order into unlabeled test instances for deep learning tasks,

Reference 10

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Observation 89487af6-3fc0-4f87-a145-fc5bf466912c · outbound

This paper cites Adaptive Test Selection for Deep Neural Networks,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Adaptive Test Selection for Deep Neural Networks,

Reference 11

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Observation 21ca4d55-d143-4014-9ccc-78971cae3c20 · outbound

This paper cites DeepSample: DNN Sampling-Based Testing for Operational Accuracy Assessment,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection DeepSample: DNN Sampling-Based Testing for Operational Accuracy Assessment,

Reference 12

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Observation 32b913e8-9442-4ccb-a4d5-0e8ba7168749 · outbound

This paper cites A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User- Adjustable Stopping,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User- Adjustable Stopping,

Reference 13

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source=pdf_text observed=2026-07-11T16:56:48.568027Z digest=sha256:cdceb14b82369a4cda2a798183f18b978506b8dc9f652729abdf2d94729e00c0

Observation c4d6080b-da08-402e-aee4-3f88612babcb · outbound

This paper cites Stopping Criterion for Active Learning Based on Deterministic Generalization Bounds,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Stopping Criterion for Active Learning Based on Deterministic Generalization Bounds,

Reference 14

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Observation e0fe363e-d22a-41fa-9903-9fb7c7eed6fb · outbound

This paper cites FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection,

Reference 15

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source=pdf_text observed=2026-07-11T16:56:48.568027Z digest=sha256:49fad5204cfad0edc945fa7c472b42093aa25d43280d27972edecea9cdfd8504

Observation 38899258-3e08-4281-a0eb-cc665df8c0db · outbound

This paper cites DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks,

Reference 16

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Observation f30ac812-7840-406b-834f-a6627e0f2159 · outbound

This paper cites CertPri: Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection CertPri: Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space,

Reference 17

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Observation b1d22a16-27fb-4eca-b1de-d90610dd0cd5 · outbound

This paper cites The SAFE Procedure: A Practical Stopping Heuristic for Active Learning-Based Screening in Systematic Reviews and Meta-Analyses,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection The SAFE Procedure: A Practical Stopping Heuristic for Active Learning-Based Screening in Systematic Reviews and Meta-Analyses,

Reference 18

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Observation d1f832a7-499b-4506-a2ca-216dbafb688e · outbound

This paper cites Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion,

Reference 19

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Observation 983ee5d1-fea9-47f4-8cb4-b960b05d9d87 · outbound

This paper cites Stopping Criterion for Active Learning Based on Error Stability.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Stopping Criterion for Active Learning Based on Error Stability

Reference 20

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Observation 97164c2d-05cb-48b9-a3c8-3a892e36843c · outbound

This paper cites Using Chao's Estimator as a Stopping Criterion for Technology-Assisted Review.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Using Chao's Estimator as a Stopping Criterion for Technology-Assisted Review

Reference 21

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Observation 83d64cdc-bbf1-43a1-a24f-cbc79c998ff2 · outbound

This paper cites Cost-Effective Testing of a Deep Learning Model through Input Reduction,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Cost-Effective Testing of a Deep Learning Model through Input Reduction,

Reference 22

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Observation 4c55d5e8-30dd-4b9a-bbb6-2a0c105f30f0 · outbound

This paper cites Adaptive Labeling for Efficient Out-of-distribution Model Evaluation,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Adaptive Labeling for Efficient Out-of-distribution Model Evaluation,

Reference 23

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Observation bbf0f71a-bd62-4927-b624-7d8a9237fc2e · outbound

This paper cites DeepTest: Automated Testing of Deep-Neural- Network-Driven Autonomous Cars,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection DeepTest: Automated Testing of Deep-Neural- Network-Driven Autonomous Cars,

Reference 24

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Observation 4cfc56ba-53f8-465c-8bff-712b26e7ec65 · outbound

This paper cites Dermatologist-level classification of skin cancer with deep neural networks,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Dermatologist-level classification of skin cancer with deep neural networks,

Reference 25

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Observation bf79f545-e16a-41b8-877e-844aff207c96 · outbound

This paper cites Deep Learning for Financial Applications : A Survey.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Deep Learning for Financial Applications : A Survey

Reference 26

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Observation 44b98465-f744-4bc1-9603-069fb90f5fdc · outbound

This paper cites Active Learning Literature Survey,.

AdaStop: Cost-Aware Early Stopping for DNN Test Selection Active Learning Literature Survey,

Reference 27

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Pith citing papers

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