Paper Citation Record · LEDGER

Why are Adaptive Methods Good for Attention Models?

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1912.03194.

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

pith.paper-citation-record.v1
1912.03194 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:35:00.591858Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T10:30:58.903033Z

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 aa39e3b4-8d9a-4070-8706-99af43b1858e · inbound

Adaptive Federated Optimization cites this paper.

Adaptive Federated Optimization Why are Adaptive Methods Good for Attention Models?

Reference 245

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:30:58.904776Z

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 0351a6d9-9cd4-48a8-8d70-89e37877fdaf · inbound

On the Performance of Differentially Private Optimization with Heavy-Tail Class Imbalance cites this paper.

On the Performance of Differentially Private Optimization with Heavy-Tail Class Imbalance Why are Adaptive Methods Good for Attention Models?

Reference 17

Resolution: malformed identifier; no resolver link, observed 2026-08-06T17:35:00.591858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4991c1e8-4429-4396-a665-f0c73e69f7f0 · inbound

Gradient Clipping Beyond Vector Norms: A Spectral Approach for Matrix-Valued Parameters cites this paper.

Gradient Clipping Beyond Vector Norms: A Spectral Approach for Matrix-Valued Parameters Why are Adaptive Methods Good for Attention Models?

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:02:27.452595Z

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 2cb0968c-b162-4856-bb1f-8958827466ae · inbound

Perspectives on Tsallis Statistics for Artificial Intelligence cites this paper.

Perspectives on Tsallis Statistics for Artificial Intelligence Why are Adaptive Methods Good for Attention Models?

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T00:33:17.824749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.