Paper Citation Record · LEDGER

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models

As of 22 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2412.08592.

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

pith.paper-citation-record.v1
2412.08592 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:48:50.108806Z

measured 28 of 28 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact0
verified fuzzy19
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c5171843-01ca-43b1-aebe-402636ef3fb2 · outbound

This paper cites Pushing data into cp models using graphical model learn- ing and solving.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Pushing data into cp models using graphical model learn- ing and solving

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.530762Z

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.

Observation baa8bf1c-77c2-47f3-a8f9-fabc4b960d83 · outbound

This paper cites Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, and et al.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, and et al

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.515832Z

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.

Observation 04339067-3047-4320-a735-271fe2499037 · outbound

This paper cites Palm: scaling language model- ing with pathways, 2022.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Palm: scaling language model- ing with pathways, 2022

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.502010Z

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.

Observation 5e2b8191-a827-48c3-9185-bebff12d160b · outbound

This paper cites Inter-subject analysis: A partial gaussian graphical model approach.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Inter-subject analysis: A partial gaussian graphical model approach

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.486774Z

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.

Observation 174d83c8-3c29-4e5e-903f-7853a7ae5bfe · outbound

This paper cites Qlora: efficient finetuning of quantized llms.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Qlora: efficient finetuning of quantized llms

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.470897Z

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.

Observation 50cc1b2d-ebe1-4043-87e2-6c2dcde4b1b6 · outbound

This paper cites Bert: pre-training of deep bidirec- tional transformers for language understanding, 2019.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Bert: pre-training of deep bidirec- tional transformers for language understanding, 2019

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.455375Z

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.

Observation 9dca2c22-5052-4972-8cb3-072c3678efe9 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:50.007577Z

Source-reported events for the cited work

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Observation bcf8befb-befa-403e-a64f-8c2e0d93af8a · outbound

This paper cites A statistical view of some chemometrics regression tools.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models A statistical view of some chemometrics regression tools

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.439242Z

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.

Observation 414021d9-c06e-4f0e-828c-09c6713df434 · outbound

This paper cites Sparse inverse covariance estimation with the graphical lasso.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Sparse inverse covariance estimation with the graphical lasso

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.422766Z

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.

Observation 1f75024f-6c21-4a0b-9871-7aab7677120f · outbound

This paper cites Fast sparse regression and classi- fication.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Fast sparse regression and classi- fication

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.407487Z

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.

Observation e638c47a-5cfe-416d-bb9c-71cf4c6181f4 · outbound

This paper cites A feasible nonconvex relaxation approach to feature selec- tion.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models A feasible nonconvex relaxation approach to feature selec- tion

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.393506Z

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.

Observation 7a1e80bf-5c96-4ae1-a696-01a8c495fd05 · outbound

This paper cites Parameter-Efficient Fine-Tuning with Discrete Fourier Transform.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Parameter-Efficient Fine-Tuning with Discrete Fourier Transform

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:50.033486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a074644-ea2e-4469-a929-72d860fa3ac0 · outbound

This paper cites Nonlinear image recovery with half-quadratic regularization.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Nonlinear image recovery with half-quadratic regularization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.378620Z

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.

Observation 6c184358-7943-40ea-aeaa-f0b8cd51de3f · outbound

This paper cites Variable selection for gaussian graphi- cal models.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Variable selection for gaussian graphi- cal models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.365759Z

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.

Observation a47a60c9-b935-4d32-93d2-ac21e422a844 · outbound

This paper cites Parameter- efficient transfer learning for nlp, 2019.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Parameter- efficient transfer learning for nlp, 2019

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:50.048196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e49fc825-d5c5-4e80-81b4-7e77c7635675 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:50.051720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e445f987-5af4-4013-8eb0-d914f4c8d0a8 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:50.055628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00dceb73-bfcd-404e-9cc4-bfe31c29ed4b · outbound

This paper cites Roberta: a robustly optimized bert pretraining approach, 2019.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Roberta: a robustly optimized bert pretraining approach, 2019

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.343109Z

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.

Observation 84996f1e-009c-45ba-9349-40aab746ed28 · outbound

This paper cites P-tuning v2: prompt tuning can be comparable to fine-tuning univer- sally across scales and tasks, 2022.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models P-tuning v2: prompt tuning can be comparable to fine-tuning univer- sally across scales and tasks, 2022

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.326994Z

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.

Observation 25b07e4e-f47e-41c7-bbc2-f6a6a0927d3a · outbound

This paper cites Iterative log thresholding, 2013.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Iterative log thresholding, 2013

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.310781Z

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.

Observation 5a3b9e53-2432-4571-8762-72de81cee101 · outbound

This paper cites PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:50.074666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fc6bde1-4e3f-46cd-b04e-a8cf516f85d8 · outbound

This paper cites Highly under- sampled magnetic resonance image reconstruction via ho- motopic ℓ0-minimization.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Highly under- sampled magnetic resonance image reconstruction via ho- motopic ℓ0-minimization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.297339Z

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.

Observation 5cce276f-e651-48c8-b552-e0f1ab8991c6 · outbound

This paper cites DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:50.085270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 355bc8c1-ad55-4cc7-a0c9-e336150e1d53 · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:50.090443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c689926-33d3-4f1e-9caf-e54262adfe87 · outbound

This paper cites Bitfit: simple parameter-efficient fine-tuning for transformer-based masked language-models, 2022.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Bitfit: simple parameter-efficient fine-tuning for transformer-based masked language-models, 2022

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.280652Z

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.

Observation a78f96bf-054f-4482-a191-46c1a2b44886 · outbound

This paper cites Platon: Pruning large transformer models with upper con- fidence bound of weight importance.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Platon: Pruning large transformer models with upper con- fidence bound of weight importance

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.265984Z

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.

Observation 252ec172-cc42-49cf-88de-117c9a2147e4 · outbound

This paper cites AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:50.104554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2823fbe0-681e-4c3c-a1e5-fb3f382c8016 · outbound

This paper cites Structured sparsity optimization with non-convex surrogates of ℓ2,0- norm: A unified algorithmic framework.

Adaptive Principal Components Allocation with the $\ell_{2,g}$-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large Models Structured sparsity optimization with non-convex surrogates of ℓ2,0- norm: A unified algorithmic framework

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:48:50.251024Z

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.

Pith citing papers

No inbound Pith citation observations are available.