Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2501.06081.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:20:45.699370Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T07:29:38.380414Z
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 87d7e869-f19e-427e-8c0a-7b832eba50bc · inbound
PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning cites this paper.
PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:45.699370Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5a54d09-e260-4771-8904-1b74d5958b9b · inbound
Central limit theorem for the averaged Adam optimizer cites this paper.
Central limit theorem for the averaged Adam optimizer Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T07:29:38.381910Z
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.