Paper Citation Record · LEDGER

A Study of Gradient Variance in Deep Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2007.04532.

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

pith.paper-citation-record.v1
2007.04532 v1

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 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:51:38.925694Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T09:45:40.144444Z

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 daf8b0b3-9c90-4ece-9d20-2d14d4ef2401 · inbound

Insights from Gradient Dynamics: Gradient Autoscaled Normalization cites this paper.

Insights from Gradient Dynamics: Gradient Autoscaled Normalization A Study of Gradient Variance in Deep Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T10:51:38.925694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7922e86-0307-40b8-9ca9-52f3962e69af · inbound

Rethinking the Harmonic Loss via Non-Euclidean Distance Layers cites this paper.

Rethinking the Harmonic Loss via Non-Euclidean Distance Layers A Study of Gradient Variance in Deep Learning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-15T13:00:00.617787Z

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.

Observation 52dce4ed-24db-435a-bc31-f80f64c8123e · inbound

VERITAS: A Multi-Agent Co-Scientist for Verifiable Image-Derived Hypothesis Testing cites this paper.

VERITAS: A Multi-Agent Co-Scientist for Verifiable Image-Derived Hypothesis Testing A Study of Gradient Variance in Deep Learning

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-12T21:34:30.078813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 260c7e6c-e257-48aa-8d76-3ae0a4508dec · inbound

Why Your Tokenizer Fails in Information Fusion: A Timing-Aware Pre-Quantization Fusion for Video-Enhanced Audio Tokenization cites this paper.

Why Your Tokenizer Fails in Information Fusion: A Timing-Aware Pre-Quantization Fusion for Video-Enhanced Audio Tokenization A Study of Gradient Variance in Deep Learning

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T11:31:01.909242Z

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.

Observation a7302547-b729-4c6b-8878-a3179a443f90 · inbound

LiteMatch: Lightweight Zero-Shot Stereo Matching via Cost Volume Stabilization cites this paper.

LiteMatch: Lightweight Zero-Shot Stereo Matching via Cost Volume Stabilization A Study of Gradient Variance in Deep Learning

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T09:45:40.145766Z

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.

Observation 707b3328-6f5e-4522-bb94-1ef4f70af353 · inbound

Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression cites this paper.

Progressive$^2$: A Teacher-Student Progressive Co-Evolving Knowledge Distillation Method for Substantial Model Compression A Study of Gradient Variance in Deep Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T00:41:45.205816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.