Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1909.13550.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T19:08:29.397257Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T21:17:48.622280Z
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 9dda301a-96b7-4b6c-a36e-fdac7b24ab30 · inbound
Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation cites this paper.
Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T19:08:29.397257Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation efba4fb0-e700-4e5c-a6ae-b97e6cdddaeb · inbound
Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout cites this paper.
Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T18:32:12.345873Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9f9ee89f-80b0-4766-8201-c9f188f73a92 · 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 Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T21:17:48.625362Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.