Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
AdaLoss: A computationally-efficient and provably convergent adaptive gradient method
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2109.08282.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:05:44.297261Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T21:05:46.832019Z
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 f0a9de00-d99e-4d07-9f82-6cb5c4cce1e1 · inbound
Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks cites this paper.
Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks AdaLoss: A computationally-efficient and provably convergent adaptive gradient method
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T20:46:05.467029Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4a427748-22ed-4098-910b-d3ec38d1d512 · inbound
On MUON optimization: From non-convergence to an error analysis with Polar Express and the Newton-Schulz polynomial from implementations cites this paper.
On MUON optimization: From non-convergence to an error analysis with Polar Express and the Newton-Schulz polynomial from implementations AdaLoss: A computationally-efficient and provably convergent adaptive gradient method
Reference 34
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T21:05:46.835080Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.