SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors is a research paper published in Proceedings of the AAAI Conference on Artificial Intelligence (2021). On theSindex it has a DataRank of 0.312. It has been cited 7 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?
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Base Score Contribution
0.312
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 →National Science Foundation
Grant: 1418511
SCH: INT: Collaborative Research: High-throughput Phenotyping on Electronic Health Records using Multi-Tensor Factorization
National Science Foundation
Grant: 1533768
XPS: FULL: DSD: A Parallel Tensor Infrastructure (ParTI!) for Data Analysis
National Institutes of Health
Grant: 1R56HL138415-01
Interpretable Deep Learning Model for Longitudinal Electronic Health Records and Applications to Heart Failure Prediction
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: 1838042
BigData:IA:Collaborative Research: TIMES: A tensor factorization platform for spatio-temporal data
FWCI
1.10
Citation Percentile
0.8%
Influential Citations
3
Citation Trend
Fields of Study
Keywords