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Paper Citation Record · LEDGER

Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2504.05758.

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

pith.paper-citation-record.v1
2504.05758 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:27:31.753442Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:07:34.561707Z

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 d0ba35de-7218-421b-8d56-69734ddff948 · 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 Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-16T11:27:31.753442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:27:31.753442Z digest=sha256:738f1f9815ef05eab489c2364e399e408620470e37d4fed8058512664e24ac55

Observation 8d79a112-b184-4fe4-83ba-fff03ebb76f1 · 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 Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-16T05:52:08.835236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:52:08.835236Z digest=sha256:e0eee461ba7e6ffbc1ed305c601c754493a56dcde86592e092e01c4296c57602

Observation 4997277f-6c60-4c40-98fa-bb72f7017ead · inbound

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

Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-16T04:49:08.728146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:49:08.728146Z digest=sha256:065e8d1c7010eccbb5fd842a299ab84856a1b19dbe102453c512f7efbc2fbe51

Observation ce8a0582-3571-4c88-8a4d-93a6324e872f · 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 Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T22:55:36.777641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:55:36.777641Z digest=sha256:58f243355981e7b6d8f923cf72d6d8c192e049cdd41f5a59b1f56fee136abbaf

Observation e24b724f-df52-4a00-be52-4cf2f2ec62d3 · 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 Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-15T22:50:04.403705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:50:04.403705Z digest=sha256:bc66f826d05f0b9b57a2a5490d781efa4daa891c5218ad350baa26aaefb37ca3

Observation c0fbfdcb-4903-4ea6-980c-2bf776069a85 · 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 Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-15T22:04:24.740575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:04:24.740575Z digest=sha256:e9eb5e3448875e125e9dacc2ed14f5fbe1f1769f8a793c03f4eee1b19690fc6e

Observation 309ba084-2cde-4a8d-a265-205028b44c6c · inbound

Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems cites this paper.

Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:07:34.701766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-07T15:07:33.818684Z digest=sha256:b677f4d75d1e98b30d76f466315482b999e83c772fd37c4c593d1061c3d99617