Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2202.01838.
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 2026-08-07T13:07:43.756190Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T18:04:57.789902Z
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 e86cf782-27fb-406d-848c-982168889d7f · inbound
Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization cites this paper.
Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:07:43.756190Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fd15921a-006a-4ca6-8b24-b9af0ec8cfbf · inbound
Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems cites this paper.
Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:58:57.125355Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8d52b00-9408-4274-86d1-c1b552cbf2f6 · inbound
TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimization cites this paper.
TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimization Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T05:43:05.490010Z
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 dea0ac3e-f9eb-4f15-bcac-a255a40dbb88 · inbound
TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimization cites this paper.
TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimization Characterizing & Finding Good Data Orderings for Fast Convergence of Sequential Gradient Methods
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T18:04:57.791645Z
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.