Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Oracle-MNIST: a Dataset of Oracle Characters for Benchmarking Machine Learning Algorithms
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2205.09442.
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 2026-08-16T00:36:25.421117Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T20:17:21.175190Z
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 a212b9a3-2c6e-4e37-b892-df70440d9242 · inbound
Vision Eagle Attention: a new lens for advancing image classification cites this paper.
Vision Eagle Attention: a new lens for advancing image classification Oracle-MNIST: a Dataset of Oracle Characters for Benchmarking Machine Learning Algorithms
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:38:32.765313Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 625a11de-094f-4bd1-a882-cd875763a363 · inbound
Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning cites this paper.
Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning Oracle-MNIST: a Dataset of Oracle Characters for Benchmarking Machine Learning Algorithms
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T18:38:28.731688Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c53d7111-ebe5-44fa-a5a4-2ad4807da16c · inbound
Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning cites this paper.
Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning Oracle-MNIST: a Dataset of Oracle Characters for Benchmarking Machine Learning Algorithms
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T16:21:16.084909Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 121271f9-bf14-43b7-9b2a-3d8ef36fbb04 · inbound
Enhancing Oracle Bone Inscription Recognition via Multi-Scale Layer Attention cites this paper.
Enhancing Oracle Bone Inscription Recognition via Multi-Scale Layer Attention Oracle-MNIST: a Dataset of Oracle Characters for Benchmarking Machine Learning Algorithms
Reference 58
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T20:17:21.177025Z
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 e9975439-4fc7-479a-9207-f2e38e040a73 · inbound
JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis cites this paper.
JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis Oracle-MNIST: a Dataset of Oracle Characters for Benchmarking Machine Learning Algorithms
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:36:25.421117Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.