Paper Citation Record · LEDGER

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors

As of 22 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2505.00580.

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

pith.paper-citation-record.v1
2505.00580 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:46:19.953428Z

measured 36 of 36 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.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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verified fuzzy12
unresolved24
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 449e1c5d-e915-40ef-90bf-b65af332a3d8 · outbound

This paper cites Lamda: Large model fine-tuning via spectrally decomposed low-dimensional adaptation,.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Lamda: Large model fine-tuning via spectrally decomposed low-dimensional adaptation,

Reference 1

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Observation d9db21bc-172d-402f-8fcc-d021bf849f94 · outbound

This paper cites An exploration of parameter redundancy in deep networks with circulant projections.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors An exploration of parameter redundancy in deep networks with circulant projections

Reference 5

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Observation 30e282b7-320e-46ea-ae38-7bfb91e42c88 · outbound

This paper cites The pascal recognising textual entailment challenge.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors The pascal recognising textual entailment challenge

Reference 7

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Observation a36b049f-6970-47c7-aa87-6b6f160416d4 · outbound

This paper cites Automatically constructing a corpus of sentential para- phrases.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Automatically constructing a corpus of sentential para- phrases

Reference 10

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Observation 51fa7349-80ed-4269-838a-1c3dbf860220 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale,.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors An image is worth 16x16 words: Trans- formers for image recognition at scale,

Reference 11

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Observation 43525bad-5213-48af-a776-68789be1111e · outbound

This paper cites Parameter-efficient fine-tuning with discrete fourier trans- form.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Parameter-efficient fine-tuning with discrete fourier trans- form

Reference 12

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Observation 9af9fa19-70be-4d8a-87ec-6ea6155ccd86 · outbound

This paper cites an unresolved cited work.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Unresolved cited work

Reference 13

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Observation c5079ce0-3b50-4445-a1de-b3d759a08b0a · outbound

This paper cites Kopiczko, Tijmen Blankevoort, and Yuki M.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Kopiczko, Tijmen Blankevoort, and Yuki M

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T04:46:19.858665Z

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Observation 9aefcffd-4c76-4e17-86e8-c8cc8a097a45 · outbound

This paper cites Learning multiple layers of features from tiny im- ages.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Learning multiple layers of features from tiny im- ages

Reference 18

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Observation 8412c2c5-4a19-42f0-bd27-2c32419edb71 · outbound

This paper cites Prefix- tuning: Optimizing continuous prompts for generation,.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Prefix- tuning: Optimizing continuous prompts for generation,

Reference 20

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Source-reported events for the cited work

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Observation 2c976661-bff1-4b48-ad87-156e54085378 · outbound

This paper cites Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare

Reference 21

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Observation 6c074a4d-0691-4b3a-83be-9d20ac692f76 · outbound

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

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Roberta: A robustly optimized bert pretraining approach,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T04:46:19.884267Z

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Observation e65bbf73-9df7-40ca-96b9-55d832ac6f0f · outbound

This paper cites Decoupled Weight Decay Regularization.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Decoupled Weight Decay Regularization

Reference 23

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Observation 6f81c30e-746f-4f57-ada3-8a59a0e3d602 · outbound

This paper cites Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks

Reference 24

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Observation 42481f8a-21a8-4770-b61f-98847755288f · outbound

This paper cites Knowledge acquired by foundation models.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Knowledge acquired by foundation models

Reference 25

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Observation 3aa68d7b-f4f2-4119-b5d3-4891032db9f0 · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Pytorch: An imperative style, high- performance deep learning library

Reference 26

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Observation eb9f3b4c-1954-419f-bfb4-19197ebd66cd · outbound

This paper cites Improving language understanding by gen- erative pre-training.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Improving language understanding by gen- erative pre-training

Reference 27

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Observation 09d456ec-8103-4d45-9053-6be8479e016d · outbound

This paper cites SQuAD: 100,000+ Questions for Machine Comprehension of Text.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors SQuAD: 100,000+ Questions for Machine Comprehension of Text

Reference 28

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Observation 378f54eb-054a-4b6f-8df8-e974cdc89a48 · outbound

This paper cites C-lstm: Enabling efficient lstm using structured com- pression techniques on fpgas.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors C-lstm: Enabling efficient lstm using structured com- pression techniques on fpgas

Reference 32

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Reference 33

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Observation bd3b5a2f-52a6-494b-aa7e-fa55ea68785f · outbound

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Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Unresolved cited work

Reference 34

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Observation 550a8836-5383-4e28-8f66-2c8f433fafa8 · outbound

This paper cites Adaptive budget allocation for parameter- efficient fine-tuning.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Adaptive budget allocation for parameter- efficient fine-tuning

Reference 35

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Observation b7a2d187-41a2-4121-bb7e-6afecaeaa286 · outbound

This paper cites Xing, Hao Zhang, Joseph E.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Xing, Hao Zhang, Joseph E

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:46:20.091222Z

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Observation 4d526488-329e-40de-ac17-5528fddbb380 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2005

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This paper cites Conditional adapters: Parameter-efficient transfer learning with fast in- ference.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Conditional adapters: Parameter-efficient transfer learning with fast in- ference

Reference 2009

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This paper cites Lst: Ladder side-tuning for parameter and mem- ory efficient transfer learning.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Lst: Ladder side-tuning for parameter and mem- ory efficient transfer learning

Reference 2013

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This paper cites Remote sensing image scene classification: Benchmark and state of the art.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Remote sensing image scene classification: Benchmark and state of the art

Reference 2015

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This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Recursive deep models for semantic compositionality over a sentiment treebank

Reference 2016

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Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors End- to-end autonomous driving: Challenges and frontiers

Reference 2017

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Observation cd6dd483-afb1-4b3e-bacd-5f239294fdca · outbound

This paper cites Circnn: accelerat- ing and compressing deep neural networks using block- circulant weight matrices.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Circnn: accelerat- ing and compressing deep neural networks using block- circulant weight matrices

Reference 2018

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This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 2019

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This paper cites SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation

Reference 2020

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This paper cites Factoring matrices into the product of circulant and diagonal matrices.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Factoring matrices into the product of circulant and diagonal matrices

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:46:20.328993Z

Source-reported events for the cited work

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Observation 767822e4-e063-4bd7-8ef8-600395cb7169 · outbound

This paper cites Llama: Open and efficient foundation language models,.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Llama: Open and efficient foundation language models,

Reference 2022

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Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Parameter-efficient transfer learning for nlp

Reference 2023

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Observation 1ab831af-6189-4734-83ea-3c0695b7be1a · outbound

This paper cites Language models are few-shot learn- ers.

Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors Language models are few-shot learn- ers

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T04:46:19.781518Z

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Pith citing papers

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