Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Spatial Uncertainty Sampling for End-to-End Control
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1805.04829.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T13:45:38.656848Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T21:17:48.530276Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 6f8aa134-3359-4881-874f-f5d560ef77ae · inbound
Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropout cites this paper.
Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropout Spatial Uncertainty Sampling for End-to-End Control
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T13:45:38.656848Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c1f2d8c1-5ec9-4f7c-9a29-0e909e81c18e · inbound
Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty cites this paper.
Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty Spatial Uncertainty Sampling for End-to-End Control
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T01:04:50.771831Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9446772d-a199-4092-b429-cdad3e4d3304 · inbound
A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design cites this paper.
A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design Spatial Uncertainty Sampling for End-to-End Control
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:38:41.379877Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 14c6c421-e682-4965-855a-f29565cddfe4 · inbound
A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning cites this paper.
A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning Spatial Uncertainty Sampling for End-to-End Control
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T21:17:48.532940Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.