Paper Citation Record · LEDGER

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2204.12511.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2204.12511 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:19:52.059484Z

measured 0 of 1 external citation measurements

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 07aca918-b385-4960-bd3c-96b7ef52ff18 · inbound

What is YOLOv6? A Deep Insight into the Object Detection Model cites this paper.

What is YOLOv6? A Deep Insight into the Object Detection Model PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T13:34:55.369556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bfe4783-7be1-4f28-acd9-eb212e020faf · inbound

Towards Explainable Spoofed Speech Attribution and Detection:a Probabilistic Approach for Characterizing Speech Synthesizer Components cites this paper.

Towards Explainable Spoofed Speech Attribution and Detection:a Probabilistic Approach for Characterizing Speech Synthesizer Components PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T23:48:28.652472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-14T06:32:32.682623+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.

Observation dc6add5d-1ca9-41e3-92c5-d46b1a4594c8 · inbound

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification cites this paper.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.059484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.