Paper Citation Record · LEDGER

A Survey of Uncertainty in Deep Neural Networks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2107.03342.

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

pith.paper-citation-record.v1
2107.03342 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:49:21.020439Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:29:37.397652Z

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 7c95cdd9-9c64-4887-997e-6ea415259b4b · inbound

A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties cites this paper.

A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties A Survey of Uncertainty in Deep Neural Networks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-24T03:58:51.506016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 17f7d5f3-4a42-46b1-8c2e-415eec18db30 · inbound

Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models? cites this paper.

Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models? A Survey of Uncertainty in Deep Neural Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T13:49:21.020439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5e7fe3b-40e3-4b08-a825-e40abf81fbb7 · inbound

Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification cites this paper.

Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification A Survey of Uncertainty in Deep Neural Networks

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:08:59.356040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a7329e2-0b63-40cb-a845-8e5fd7ed970a · inbound

Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning cites this paper.

Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning A Survey of Uncertainty in Deep Neural Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:58:29.226049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04f9addb-02fb-4c4b-8359-a9a00f7f385a · inbound

Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modelling cites this paper.

Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modelling A Survey of Uncertainty in Deep Neural Networks

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:29:37.399150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 170b5c24-44fe-4ff2-8dbd-5d1ef0134aaa · inbound

Controllable Diversity in Normalization-Based Implicit Ensembles via Softmax-Temperature Modulation cites this paper.

Controllable Diversity in Normalization-Based Implicit Ensembles via Softmax-Temperature Modulation A Survey of Uncertainty in Deep Neural Networks

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-07-30T14:07:56.700511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.