Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1806.06763.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T13:57:35.421758Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T00:27:30.236644Z
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 c2d172a0-7a86-44f0-b5d2-94bbff2dc5fd · inbound
Principled Approximation Methods for Efficient and Scalable Deep Learning cites this paper.
Principled Approximation Methods for Efficient and Scalable Deep Learning Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Reference 1989
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T13:57:35.421758Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e2a562c2-310a-470c-ada3-3d8e94ebc789 · inbound
Anon: Extrapolating Adaptivity Beyond SGD and Adam cites this paper.
Anon: Extrapolating Adaptivity Beyond SGD and Adam Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T16:31:09.744246Z
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 49c717a9-fb29-49d1-ba6d-3a41250750b2 · inbound
\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments cites this paper.
\mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Reference 50
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T02:30:55.196260Z
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 fe693544-427e-4602-9b2c-d748493439a5 · inbound
Muon Learns More Robust and Transferable Features than Adam cites this paper.
Muon Learns More Robust and Transferable Features than Adam Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Reference 65
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T00:27:30.237916Z
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 c543a109-4fbb-435d-b93a-1b1e3ab6118f · inbound
OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers cites this paper.
OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T22:10:49.683444Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.