SCRIB: Set-Classifier with Class-Specific Risk Bounds for Blackbox Models is a research paper published in Proceedings of the AAAI Conference on Artificial Intelligence (2022). On theSindex it has a DataRank of 0.241. It has been cited 4 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.241
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →NHLBI NIH HHS
Grant: R01 HL161253
NINDS NIH HHS
Grant: R01 NS102190
NINDS NIH HHS
Grant: R01 NS126282
NINDS NIH HHS
Grant: R01 NS107291
NINDS NIH HHS
Grant: RF1 NS120947
National Science Foundation
Grant: 1838042
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National Science Foundation
Grant: 2014438
SCH:INT: Collaborative Research: Deep Sense: Interpretable Deep Learning for Zero-effort Phenotype Sensing and Its Application to Sleep Medicine
National Science Foundation
Grant: 2028839
Collaborative Research: PPoSS: Planning: Integrated Scalable Platform for Privacy-aware Collaborative Learning and Inference
FWCI
1.48
Citation Percentile
0.8%
Citation Trend
Fields of Study
Keywords