Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2204.12511.
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 2026-08-07T11:56:33.693707Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T21:51:11.838674Z
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 0f28b408-3302-4933-9065-e3a22ee6c58a · inbound
ECP-Mamba: An Efficient Multi-scale Self-supervised Contrastive Learning Method with State Space Model for PolSAR Image Classification cites this paper.
ECP-Mamba: An Efficient Multi-scale Self-supervised Contrastive Learning Method with State Space Model for PolSAR Image Classification PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:56:33.693707Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f7069a1-e632-496a-a9aa-2cd5702003d2 · inbound
ViPO: Visual Preference Optimization at Scale cites this paper.
ViPO: Visual Preference Optimization at Scale PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:51:11.843562Z
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 9395f6b6-af73-43d3-ac41-3f1d4ba5a549 · inbound
Multi-Modal Semantic Segmentation of Electrolyzer Components for Sustainable Hydrogen Technologies: A Dual-Branch Deep Learning Approach cites this paper.
Multi-Modal Semantic Segmentation of Electrolyzer Components for Sustainable Hydrogen Technologies: A Dual-Branch Deep Learning Approach PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T21:31:50.695019Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.