Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2307.00497.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:26:58.148077Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T07:35:57.501256Z
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 2b664953-d285-4a21-9aa7-a214b6ba254a · inbound
Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting cites this paper.
Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:26:58.148077Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cb5bf870-7b70-453c-ae63-249b8bff0e8e · inbound
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity cites this paper.
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T07:35:57.503283Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.