Paper Citation Record · LEDGER

Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2504.04032.

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

pith.paper-citation-record.v1
2504.04032 v1

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 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:27:31.758257Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-15T22:04:24.976579Z

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 51b3d4e7-3aa1-410c-8065-f1d812a2cb03 · inbound

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions cites this paper.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T11:27:31.758257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28102bfe-3154-4a5e-aeb1-22e194aa43fc · inbound

Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning cites this paper.

Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T05:52:08.828648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48c79814-7820-457d-a61e-466a2748f6d8 · inbound

Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning cites this paper.

Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T04:49:08.737405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8795c170-4b4f-438c-a261-b57d1dda8f6b · inbound

Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks cites this paper.

Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:55:36.749538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af8e2e52-f3dc-4401-9d45-ef1dfd85a38b · inbound

Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies cites this paper.

Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:04.430239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36173cac-d91e-4667-b7f0-769aea266f37 · inbound

Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks cites this paper.

Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:04:24.987101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.