Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1912.00018.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T00:23:05.314264Z
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 13d035c6-4109-4359-8599-e2fe27fa8e5d · inbound
Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping cites this paper.
Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:23:05.314264Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+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.