Paper Citation Record · LEDGER

On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1912.00018.

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

pith.paper-citation-record.v1
1912.00018 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 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:40:42.523157Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T20:13:58.888620Z

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 c03ac2ed-81f4-4cef-8001-8964a1fe084f · inbound

Improving Adaptive Moment Optimization via Preconditioner Diagonalization cites this paper.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T12:40:42.523157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1e1a26a-3720-4eab-98bc-efe85bfa65d0 · inbound

Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise cites this paper.

Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T10:58:36.728931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cc6652f-41cd-4f1c-90d8-7b2e9c01dedd · inbound

Nonconvex Decentralized Stochastic Bilevel Optimization under Heavy-Tailed Noise cites this paper.

Nonconvex Decentralized Stochastic Bilevel Optimization under Heavy-Tailed Noise On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T16:23:21.855702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9992b30e-ae98-4c6d-a923-3310704429e8 · inbound

Convergence of Stochastic Gradient Descent with mini-batching and infinite variance cites this paper.

Convergence of Stochastic Gradient Descent with mini-batching and infinite variance On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:45:58.098225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 38f37bd4-9a2a-4c8c-90a0-db1c9d5a1a3a · inbound

Statistical Inference for Stochastic Gradient Descent: Beyond Finite Variance cites this paper.

Statistical Inference for Stochastic Gradient Descent: Beyond Finite Variance On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T20:13:58.890159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bc0893dc-d079-48ed-a590-6eb5e1361b4d · inbound

Statistical Inference for Stochastic Gradient Descent: Beyond Finite Variance cites this paper.

Statistical Inference for Stochastic Gradient Descent: Beyond Finite Variance On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T00:56:17.858813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.