Enhancing Cost Efficiency in Active Learning with Candidate Set Query | Paper Citation Record

Observation 6529d3ff-be57-4854-9db2-4ea467355a05 · outbound

This paper cites Conformal prediction: A gentle introduction.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Conformal prediction: A gentle introduction

Reference 1

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Observation e542cb89-518b-41d6-9f72-710906ee76a9 · outbound

This paper cites Uncertainty sets for image classifiers using conformal prediction.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Uncertainty sets for image classifiers using conformal prediction

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.698865Z

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Observation 0f278c5e-8774-4afc-9dbc-89b7e5794629 · outbound

This paper cites Estimating annotation cost for active learning in a multi-annotator environment.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Estimating annotation cost for active learning in a multi-annotator environment

Reference 3

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Observation e3593199-84f7-4ad9-b6ee-080d3c61edb9 · outbound

This paper cites Deep active learning for dialogue generation.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Deep active learning for dialogue generation

Reference 4

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Observation d95252b4-61c8-424f-a37a-25bba7dcedcc · outbound

This paper cites Deep batch active learning by diverse, uncertain gradient lower bounds.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Deep batch active learning by diverse, uncertain gradient lower bounds

Reference 5

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Observation 397b87bc-b8c0-4119-8483-e941bd38663e · outbound

This paper cites Products-10K: A Large-scale Product Recognition Dataset.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Products-10K: A Large-scale Product Recognition Dataset

Reference 6

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Observation 91233ef6-e0e2-4e22-98e1-2408c2e59251 · outbound

This paper cites Active learning with n-ary queries for image recognition.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active learning with n-ary queries for image recognition

Reference 7

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Observation 578dbf97-be6c-4054-981d-20e628895cdc · outbound

This paper cites an unresolved cited work.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Unresolved cited work

Reference 8

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 70f06da1-de30-40f9-9fb6-2b6056be4ceb · outbound

This paper cites A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

Reference 9

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Observation 723f4b24-69f6-43e6-bfb2-7aeff52ed5e1 · outbound

This paper cites Support-vector networks.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Support-vector networks

Reference 10

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Observation 9d5fbcef-1b9f-49c0-94ff-97a734444e81 · outbound

This paper cites Learning from partial labels.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Learning from partial labels

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.368255Z

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Observation 4548ebb1-6ab8-4b07-b215-3d41231cc827 · outbound

This paper cites Class-balanced loss based on effective number of samples.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Class-balanced loss based on effective number of samples

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.358905Z

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Observation 8a1cfe8c-5dda-4887-b80f-e42c577bea7f · outbound

This paper cites Two faces of active learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Two faces of active learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.348346Z

Source-reported events for the cited work

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Observation ed1d7f6c-def8-4e5a-80f2-bf63bd3c8b9d · outbound

This paper cites ImageNet: a large-scale hierarchical image database.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query ImageNet: a large-scale hierarchical image database

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.338528Z

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Observation d993fe9c-d139-4cda-8f5c-df25e4cef605 · outbound

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

Enhancing Cost Efficiency in Active Learning with Candidate Set Query An image is worth 16x16 words: Transformers for image recognition at scale

Reference 15

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Observation 1f2c438a-f06d-41ca-91d1-cb0a586d2890 · outbound

This paper cites Contrastive coding for active learning under class distribution mismatch.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Contrastive coding for active learning under class distribution mismatch

Reference 16

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Observation f637239d-bb92-466e-a4ce-ebcfde78313e · outbound

This paper cites Data determines distributional robustness in contrastive language image pre-training (clip).

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Data determines distributional robustness in contrastive language image pre-training (clip)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.311838Z

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Observation 742439e9-c4fa-4e58-8667-ba1f2bc19605 · outbound

This paper cites Classification in the presence of label noise: a survey.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Classification in the presence of label noise: a survey

Reference 18

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Observation 4881aa4b-a4f2-49d5-94f4-bfc428341d2d · outbound

This paper cites a ger, Bertrand Charpentier, Antonio Oroz, and Stephan G \.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query a ger, Bertrand Charpentier, Antonio Oroz, and Stephan G \

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.297386Z

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Observation 94c388c9-953d-4e6f-bae7-3d7fc937db22 · outbound

This paper cites How to select which active learning strategy is best suited for your specific problem and budget.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query How to select which active learning strategy is best suited for your specific problem and budget

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.287784Z

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Observation e77d1be2-8641-4230-9494-e0b8a8242011 · outbound

This paper cites Active learning on a budget: Opposite strategies suit high and low budgets.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active learning on a budget: Opposite strategies suit high and low budgets

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.277800Z

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Observation 6eae3b68-bb14-4e31-88cc-87ac72171c4a · outbound

This paper cites Theory of disagreement-based active learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Theory of disagreement-based active learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.268928Z

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Observation fbafc429-723b-4918-8d5e-292ef1149f31 · outbound

This paper cites Deep residual learning for image recognition.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Deep residual learning for image recognition

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.204546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ad03d90-ddc1-4146-a2fe-5c54a2fc70b6 · outbound

This paper cites Towards better uncertainty sampling: Active learning with multiple views for deep convolutional neural network.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Towards better uncertainty sampling: Active learning with multiple views for deep convolutional neural network

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.093859Z

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Observation 74397a98-4f1d-426f-83a4-66df2288022d · outbound

This paper cites A survey on cost types, interaction schemes, and annotator performance models in selection algorithms for active learning in classification.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query A survey on cost types, interaction schemes, and annotator performance models in selection algorithms for active learning in classification

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:28.031213Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation efc134c8-ad90-4cf4-8ab7-54c8abf1928a · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Deep Learning Scaling is Predictable, Empirically

Reference 26

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Source-reported events for the cited work

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Observation 2ee8cc24-a034-4dc4-81d7-abc9007d19c8 · outbound

This paper cites One-bit supervision for image classification.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query One-bit supervision for image classification

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.977471Z

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Observation f74cb6bf-de21-4247-b4de-9519f25bf41c · outbound

This paper cites Squeeze-and-excitation networks.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Squeeze-and-excitation networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.969058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a4903c89-44a4-486c-951a-df2708b56959 · outbound

This paper cites Multi-label active learning: query type matters.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Multi-label active learning: query type matters

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.960063Z

Source-reported events for the cited work

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Observation 641c93dc-35d7-475f-9963-c4cd07e03918 · outbound

This paper cites Combating label distribution shift for active domain adaptation.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Combating label distribution shift for active domain adaptation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.950287Z

Source-reported events for the cited work

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Observation a38b4f2f-51d9-41b6-b4f5-a8dde7daa2f8 · outbound

This paper cites Active learning for semantic segmentation with multi-class label query.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active learning for semantic segmentation with multi-class label query

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.939301Z

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Observation 39519f4e-224b-4858-a731-5ab25fe0fc9c · outbound

This paper cites Breaking the interactive bottleneck in multi-class classification with active selection and binary feedback.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Breaking the interactive bottleneck in multi-class classification with active selection and binary feedback

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.929053Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 55f65268-81e9-471b-9f46-03d637dd0653 · outbound

This paper cites Active learning with complementary sampling for instructing class-biased multi-label text emotion classification.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active learning with complementary sampling for instructing class-biased multi-label text emotion classification

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.919000Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 475c69db-0b7c-4ece-a7a0-d07c77b6f3a4 · outbound

This paper cites Active label correction for semantic segmentation with foundation models.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active label correction for semantic segmentation with foundation models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.908254Z

Source-reported events for the cited work

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Observation 4fbfe951-e2ec-4ff7-bd71-3abd5e043c21 · outbound

This paper cites Saal: sharpness-aware active learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Saal: sharpness-aware active learning

Reference 35

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Source-reported events for the cited work

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Observation d74e9c76-b0a3-4b9d-a35f-ec64e510d389 · outbound

This paper cites Nlnl: Negative learning for noisy labels.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Nlnl: Negative learning for noisy labels

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 03d45779-2e84-49d9-befb-59b0aba9a2ce · outbound

This paper cites Segment anything.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Segment anything

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bf348e7e-b6fc-4760-abac-c7b8409b6165 · outbound

This paper cites Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.730764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1c6326a0-57a3-444a-b167-a9705efb3b22 · outbound

This paper cites Similar: Submodular information measures based active learning in realistic scenarios.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Similar: Submodular information measures based active learning in realistic scenarios

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.692167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b4f21eac-42da-468e-8c77-176fd8fb9f46 · outbound

This paper cites Active learning for cost-sensitive classification.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active learning for cost-sensitive classification

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.681448Z

Source-reported events for the cited work

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Observation ccf53426-f261-4bd4-8f40-07479f720d37 · outbound

This paper cites Learning multiple layers of features from tiny images.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Learning multiple layers of features from tiny images

Reference 41

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Source-reported events for the cited work

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Observation 147481fc-2bb5-4cef-adf4-3145b50c6dba · outbound

This paper cites an unresolved cited work.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:28:27.665876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4672533a-8ef4-4261-9c63-9175a8d1427f · outbound

This paper cites Generative adversarial active learning for unsupervised outlier detection.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Generative adversarial active learning for unsupervised outlier detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.655920Z

Source-reported events for the cited work

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Observation d9e24139-81be-4c9a-b263-7a6c44dd46f0 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 44

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Source-reported events for the cited work

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Observation 58992014-fd87-46ad-b8d1-6977c1049d00 · outbound

This paper cites Decoupled weight decay regularization.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Decoupled weight decay regularization

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.645883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5ff159ae-a87e-491b-9942-a461f2a56567 · outbound

This paper cites An introduction to information retrieval.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query An introduction to information retrieval

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-08T16:28:26.322692Z

Source-reported events for the cited work

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Observation 6a37ebbc-b109-40d3-9689-e7758424048e · outbound

This paper cites Conformal prediction based active learning by linear regression optimization.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Conformal prediction based active learning by linear regression optimization

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.629891Z

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Observation 14bfdfde-2c43-4ef3-93ca-97b72586537a · outbound

This paper cites Active learning for open-set annotation.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active learning for open-set annotation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.620519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 42b0f280-bb92-4851-a884-25451374b810 · outbound

This paper cites GPT-4 Technical Report.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query GPT-4 Technical Report

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-08T16:28:26.333161Z

Source-reported events for the cited work

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Observation 95b8d170-9480-4753-b974-5ee015e75e3c · outbound

This paper cites Activelink: deep active learning for link prediction in knowledge graphs.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Activelink: deep active learning for link prediction in knowledge graphs

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.611672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 52a74e1f-5472-4dc5-89de-0da66bb83e05 · outbound

This paper cites Meta-query-net: Resolving purity-informativeness dilemma in open-set active learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Meta-query-net: Resolving purity-informativeness dilemma in open-set active learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.601603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4c6408a3-6ebc-4ffc-aa19-3f058b0f14ab · outbound

This paper cites Active learning from relative queries.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active learning from relative queries

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.591091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d9c39f74-ac80-42e4-8313-d839f8c5f237 · outbound

This paper cites Abdomenatlas-8k: Annotating 8,000 ct volumes for multi-organ segmentation in three weeks.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Abdomenatlas-8k: Annotating 8,000 ct volumes for multi-organ segmentation in three weeks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.581601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 58c35231-ffa2-448a-9871-aa5939a674af · outbound

This paper cites Learning transferable visual models from natural language supervision.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Learning transferable visual models from natural language supervision

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.570272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5e571591-55ab-4f53-8649-4407c7f68443 · outbound

This paper cites Classification with valid and adaptive coverage.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Classification with valid and adaptive coverage

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.460866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ea7660c3-2fa7-4b86-b86b-7c49ff96881e · outbound

This paper cites Active learning for convolutional neural networks: A core-set approach.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active learning for convolutional neural networks: A core-set approach

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.380365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 50306659-ebfe-4e5c-b321-69a6a5b8f3dd · outbound

This paper cites Active learning literature survey.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active learning literature survey

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T16:28:26.359370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3aa417f2-68cc-4810-84c2-903d811c8185 · outbound

This paper cites Active learning with real annotation costs.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active learning with real annotation costs

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.311335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 45284d03-481f-468f-9f34-2cc063ded2d8 · outbound

This paper cites A tutorial on conformal prediction.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query A tutorial on conformal prediction

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T16:28:26.366370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03f6a0c9-7326-4ff2-a68d-794d9faf3c20 · outbound

This paper cites Variational adversarial active learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Variational adversarial active learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.257969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 248289ce-5b13-43d9-8bb0-1dcf01cb06be · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.248881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 813da31d-2409-4735-9262-09fa04abd440 · outbound

This paper cites Bayesian generative active deep learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Bayesian generative active deep learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.240127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8ebd3e40-c645-48d9-bd82-f35d2c0ebb2e · outbound

This paper cites Machine-learning applications of algorithmic randomness.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Machine-learning applications of algorithmic randomness

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.231071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2461ef6c-9d5b-4ea6-8551-b705fbcc8b5a · outbound

This paper cites Who should label what? instance allocation in multiple expert active learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Who should label what? instance allocation in multiple expert active learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.222402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 32ebad98-9090-4c5d-b090-8d5a0f5e92f3 · outbound

This paper cites Samrs: Scaling-up remote sensing segmentation dataset with segment anything model.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Samrs: Scaling-up remote sensing segmentation dataset with segment anything model

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.214573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1a6124c6-6166-44d9-88f0-f12d4cbe5c61 · outbound

This paper cites Uncertainty-based active learning for reading comprehension.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Uncertainty-based active learning for reading comprehension

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.206316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0a1c3c07-100a-4e7c-9bf7-6459ea615754 · outbound

This paper cites Incorporating distribution matching into uncertainty for multiple kernel active learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Incorporating distribution matching into uncertainty for multiple kernel active learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.198098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3f3160f5-7a6c-4675-ac1c-11fc523db030 · outbound

This paper cites Querying discriminative and representative samples for batch mode active learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Querying discriminative and representative samples for batch mode active learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.188504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ae68501e-e249-4c87-9cce-13d9859cf77c · outbound

This paper cites Multi-label learning with pairwise relevance ordering.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Multi-label learning with pairwise relevance ordering

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.179550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a4c52f7e-4577-4485-bb15-a2bdc6715b91 · outbound

This paper cites Not all out-of-distribution data are harmful to open-set active learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Not all out-of-distribution data are harmful to open-set active learning

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.170048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1cfd01a8-c74f-4163-95ae-ead95d063be4 · outbound

This paper cites Active learning through a covering lens.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Active learning through a covering lens

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.160164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c4109eff-ab11-4dd6-9c90-ac0323914e7c · outbound

This paper cites Cmal: Cost-effective multi-label active learning by querying subexamples.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Cmal: Cost-effective multi-label active learning by querying subexamples

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:27.073076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a6938536-012a-4eff-94d3-32f93e186138 · outbound

This paper cites Wide Residual Networks.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Wide Residual Networks

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-08T16:28:26.684105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 882688bc-d806-4f4b-a056-e46ea708428d · outbound

This paper cites Scaling vision transformers.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Scaling vision transformers

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:26.981939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 68d05b33-16ff-4bde-b5c7-b1a043c2e541 · outbound

This paper cites mixup: Beyond empirical risk minimization.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query mixup: Beyond empirical risk minimization

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:26.875867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bf03e414-acb0-4730-97b4-af5acebf0a9b · outbound

This paper cites Labelbench: A comprehensive framework for benchmarking adaptive label-efficient learning.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query Labelbench: A comprehensive framework for benchmarking adaptive label-efficient learning

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:26.795282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3bbe770a-4d1a-4605-81dc-42fcaab6d4d6 · outbound

This paper cites One-bit active query with contrastive pairs.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query One-bit active query with contrastive pairs

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:28:26.785448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6a4ef82e-26fa-4af2-96ae-96a4ce443c78 · outbound

This paper cites write newline.

Enhancing Cost Efficiency in Active Learning with Candidate Set Query write newline

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-08T16:28:26.700206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers