Geometric Analysis of Uncertainty Sampling for Dense Neural Network Layer is a research paper published in IEEE Signal Processing Letters (2021). 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?
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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 →National Institutes of Health
Grant: R01DC009834
Defense Advanced Research Projects Agency
Grant: SC1821301
NSF
Grant: CNS-1544895
NSF
Grant: IIS-1715858
NSF
Grant: IIS-1717654
NSF
Grant: IIS-1844885
NSF
Grant: IIS-1915083
NINDS NIH HHS
Grant: R01 NS111744
National Science Foundation
Grant: 1915083
SCH: INT: Collaborative Research: Detection, Assessment and Rehabilitation of Stroke-Induced Visual Neglect Using Augmented Reality (AR) and Electroencephalography (EEG)
National Science Foundation
Grant: 1717654
CHS: Small: Collaborative Research: EEG-Guided Electrical Stimulation for Immersive Virtual Reality
National Institutes of Health
Grant: 5R01DC009834-14
Optimizing BCI-FIT: Brain Computer Interface - Functional Implementation Toolkit
National Science Foundation
Grant: 1544895
CPS: TTP Option: Synergy: Collaborative Research: Nested Control of Assistive Robots through Human Intent Inference
National Science Foundation
Grant: 1844885
CAREER: Towards a Biologically Informed Intervention for Emotionally Dysregulated Adolescents and Adults with Autism Spectrum Disorder
National Science Foundation
Grant: 1715858
CHS: Small: Collaborative Research: EEG-Guided Electrical Stimulation for Immersive Virtual Reality
FWCI
0.54
Citation Percentile
0.7%
Citation Trend
Fields of Study
Keywords