Pith. sign in

Paper Citation Record · LEDGER

An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2110.03260.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2110.03260 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:17:20.520110Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T23:54:54.767298Z

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 b7d0e4a6-45d9-4bec-b4a9-67c3811b2cf6 · inbound

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification cites this paper.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-07T15:17:20.520110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:17:20.520110Z digest=sha256:66599aa804244782f9da5d80a6c9f2db6edbb4cdb02620c0357c011ca6dc51fe

Observation 6d1df607-c1aa-4377-b7ce-6bcbab1e82e0 · inbound

CLUE: Neural Networks Calibration via Learning Uncertainty-Error alignment cites this paper.

CLUE: Neural Networks Calibration via Learning Uncertainty-Error alignment An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-07T13:06:49.473176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:06:49.473176Z digest=sha256:c622aca76f395f0d954bfdeffe38d700392e91ca8bb1fcff0ff48c1ff20e9154

Observation 6f965a4a-c168-40b3-9990-5184a615c501 · inbound

Uncertainty Estimation by Human Perception versus Neural Models cites this paper.

Uncertainty Estimation by Human Perception versus Neural Models An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions

Reference 35

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T23:54:54.890016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:54:54.080127Z digest=sha256:0cc7d7cb3b391436bdc6572541823d96a56450b1e463ea67099458a4d849f3ba