Paper Citation Record · LEDGER

LoRA Dropout as a Sparsity Regularizer for Overfitting Control

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2404.09610.

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

pith.paper-citation-record.v1
2404.09610 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:18:24.399979Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T14:08:21.932896Z

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 bbe6b508-73c1-4b3d-bea1-d6cd1d211362 · inbound

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey cites this paper.

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 88

Resolution: verified exact; arxiv_id, observed 2026-05-13T11:32:36.999296Z

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 0a02ca4b-0986-4c10-a5b1-f3a3bbadb8d8 · inbound

Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation cites this paper.

Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:24.399979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3cbc716-6122-4f52-9c7d-2bdf49e20e66 · inbound

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead cites this paper.

Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 200

Resolution: unresolved; no resolver link, observed 2026-08-06T21:36:38.028151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3bd287b-246f-4302-82ab-37d7c445573d · inbound

RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding cites this paper.

RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:05.918085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 733922c7-e768-42c4-8661-de32d0abee3d · inbound

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model cites this paper.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T20:53:19.543142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30750dc6-100e-4404-9dd6-3bb19de6cbfa · inbound

Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation cites this paper.

Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:41:25.353792Z

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 af254bd9-57ca-49e1-b0f7-5253de3d2a74 · inbound

A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search cites this paper.

A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T09:45:42.953279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69efa364-4044-47c3-aac2-25f379122cc5 · inbound

PureCC: Pure Learning for Text-to-Image Concept Customization cites this paper.

PureCC: Pure Learning for Text-to-Image Concept Customization LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-21T11:10:02.195293Z

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 5c02c39e-0076-45e3-a55d-c0eb59e54b68 · inbound

The Hidden Power of Scaling Factor in LoRA Optimization cites this paper.

The Hidden Power of Scaling Factor in LoRA Optimization LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 84

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:08:21.934172Z

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 316086ef-5861-4be6-8b90-bccb62e2e1dd · inbound

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery cites this paper.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T18:33:56.909201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.