Paper Citation Record · LEDGER

Energy Considerations for Large Pretrained Neural Networks

As of 20 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2506.01311.

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

pith.paper-citation-record.v1
2506.01311 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:50:51.123354Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:31:49.036240Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T10:31:49.649774Z

Reference resolution

42 of 42 outbound references displayed

verified exact0
verified fuzzy36
unresolved6
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8ec6e306-e402-4d28-a540-2294f571891b · outbound

This paper cites On the steganographic capacity of selected learning models.

Energy Considerations for Large Pretrained Neural Networks On the steganographic capacity of selected learning models

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:55.067007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 414ceac2-4052-40f6-8778-857eb8b371d7 · outbound

This paper cites an unresolved cited work.

Energy Considerations for Large Pretrained Neural Networks Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:50:54.982830Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5e0c1006-e1c1-4d15-90f3-c507112476bc · outbound

This paper cites ASHRAE, Atlanta, GA, 2014.

Energy Considerations for Large Pretrained Neural Networks ASHRAE, Atlanta, GA, 2014

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.898489Z

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Observation 1ab3768b-f686-492f-914f-d05646c93a8c · outbound

This paper cites Springer, 2019.

Energy Considerations for Large Pretrained Neural Networks Springer, 2019

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.841488Z

Source-reported events for the cited work

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Observation fdb25459-b2a4-4a71-9686-9952a40ce8c5 · outbound

This paper cites Bender, D.

Energy Considerations for Large Pretrained Neural Networks Bender, D

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.698846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation caaa8b2f-5de5-42a8-b841-d1c5840d993d · outbound

This paper cites Bench- mark analysis of representative deep neural network architectures.IEEE Access, 6:64270–64277, 2018.

Energy Considerations for Large Pretrained Neural Networks Bench- mark analysis of representative deep neural network architectures.IEEE Access, 6:64270–64277, 2018

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.601052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b81ac849-e617-40c8-a9f2-706b70c20704 · outbound

This paper cites Uptime Institute Blog: Global PUEs — Are they going anywhere?https://journal.uptimeinstitute.com/global-pues-are- they-going-anywhere/, 2023.

Energy Considerations for Large Pretrained Neural Networks Uptime Institute Blog: Global PUEs — Are they going anywhere?https://journal.uptimeinstitute.com/global-pues-are- they-going-anywhere/, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.548076Z

Source-reported events for the cited work

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Observation 9465d394-cab3-4974-9d5e-595fbf93d687 · outbound

This paper cites Language models are few-shot learners.

Energy Considerations for Large Pretrained Neural Networks Language models are few-shot learners

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.505414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8fe7bd8b-6415-4728-9e04-6f663ad8a052 · outbound

This paper cites Model compression.

Energy Considerations for Large Pretrained Neural Networks Model compression

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.444737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0013aeb8-0afa-432f-820d-ec9e4118545b · outbound

This paper cites Model compression and acceleration for deep neural networks: The principles, progress, and challenges.IEEE Signal Processing Magazine, 35(1):126–136, 2018.

Energy Considerations for Large Pretrained Neural Networks Model compression and acceleration for deep neural networks: The principles, progress, and challenges.IEEE Signal Processing Magazine, 35(1):126–136, 2018

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.370687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1fdd04b5-6cf7-4fa9-beb1-5745d5bf0088 · outbound

This paper cites ImageNet: A large-scale hierarchical image database.

Energy Considerations for Large Pretrained Neural Networks ImageNet: A large-scale hierarchical image database

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.319341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 253b18f1-a1fc-48ae-a1b9-d2fbe39e93aa · outbound

This paper cites Exploiting linear structure within convolutional net- works for efficient evaluation.

Energy Considerations for Large Pretrained Neural Networks Exploiting linear structure within convolutional net- works for efficient evaluation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.266789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e925be54-c4c0-49f5-a0cf-1bdd18879188 · outbound

This paper cites The approximation of one matrix by another of lower rank.Psychometrika, 1(3):211–218, 1936.

Energy Considerations for Large Pretrained Neural Networks The approximation of one matrix by another of lower rank.Psychometrika, 1(3):211–218, 1936

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.206790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c1c9fff3-e068-44b2-b251-d92fb6ba8976 · outbound

This paper cites Peer review of GPT-4 technical report and systems card.PLOS Digital Health, 3(1):e0000417, 2024.

Energy Considerations for Large Pretrained Neural Networks Peer review of GPT-4 technical report and systems card.PLOS Digital Health, 3(1):e0000417, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:54.095227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1d3d3b42-da8b-43c6-9f69-ca868d78bbe8 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Energy Considerations for Large Pretrained Neural Networks Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:49.370242Z

Source-reported events for the cited work

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Observation 8f545028-48f6-40d6-9a1a-9ef5ccb3e9b8 · outbound

This paper cites Deep residual learning for image recognition.

Energy Considerations for Large Pretrained Neural Networks Deep residual learning for image recognition

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:53.956823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 44eaca08-6fb7-48eb-b5a6-7378a47de950 · outbound

This paper cites Model complexity of deep learning: A survey.Knowledge and Information Systems, 63(10):2585–2619, 2021.

Energy Considerations for Large Pretrained Neural Networks Model complexity of deep learning: A survey.Knowledge and Information Systems, 63(10):2585–2619, 2021

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:53.872054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e22073fb-56dd-4b9f-9b76-cbe7deae1cad · outbound

This paper cites Wein- berger.

Energy Considerations for Large Pretrained Neural Networks Wein- berger

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:53.775588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9040d922-e161-48d7-b39e-631dabe5a67e · outbound

This paper cites Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks.

Energy Considerations for Large Pretrained Neural Networks Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:49.600122Z

Source-reported events for the cited work

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Observation e29a4e0c-f820-4b87-8ef9-e5d65a5673f7 · outbound

This paper cites an unresolved cited work.

Energy Considerations for Large Pretrained Neural Networks Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:50:53.680774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 39f1db52-8718-4f92-88f5-0acb7485e200 · outbound

This paper cites Pruning filters with L1-norm and capped L1-norm for CNN compression.Applied Intelligence, 51(2):1152–1160, 2021.

Energy Considerations for Large Pretrained Neural Networks Pruning filters with L1-norm and capped L1-norm for CNN compression.Applied Intelligence, 51(2):1152–1160, 2021

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:53.573664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f1633c41-5cbb-4e3a-8a54-76fcb3dca99f · outbound

This paper cites Deep learning.Nature, 521(7553):436–444, 2015.

Energy Considerations for Large Pretrained Neural Networks Deep learning.Nature, 521(7553):436–444, 2015

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:53.489485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 379d8722-d89f-4bac-8f62-3106cdc23aae · outbound

This paper cites On-demand deep model compression for mobile devices: A usage-driven model selection framework.

Energy Considerations for Large Pretrained Neural Networks On-demand deep model compression for mobile devices: A usage-driven model selection framework

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:53.391924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f1f61de8-9212-4dd3-b9d0-c05a74078d9b · outbound

This paper cites A ConvNet for the 2020s.

Energy Considerations for Large Pretrained Neural Networks A ConvNet for the 2020s

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:53.273228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd7d3268-0a92-4116-93b5-74d82f429c16 · outbound

This paper cites A survey of related research on compression and acceleration of deep neural networks.Journal of Physics: Conference Series, 1213(5):052003, 2019.

Energy Considerations for Large Pretrained Neural Networks A survey of related research on compression and acceleration of deep neural networks.Journal of Physics: Conference Series, 1213(5):052003, 2019

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:53.167404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d622c793-ba60-4903-a9c6-028ccb3370cb · outbound

This paper cites Deep neural networks compression: A com- parative survey and choice recommendations.Neurocomputing, 520:152–170, 2023.

Energy Considerations for Large Pretrained Neural Networks Deep neural networks compression: A com- parative survey and choice recommendations.Neurocomputing, 520:152–170, 2023

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:53.071977Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6ed0380d-7c64-4422-b63e-44c6db704c49 · outbound

This paper cites Artificial Intelligence Index Report 2023.

Energy Considerations for Large Pretrained Neural Networks Artificial Intelligence Index Report 2023

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:50:50.056738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 464da764-4ea4-4017-a3bc-46ef02b1fb55 · outbound

This paper cites Information hiding: Steganography and watermarking— Attacks and countermeasures.Journal of Electronic Imaging, 10(3):825, 2001.

Energy Considerations for Large Pretrained Neural Networks Information hiding: Steganography and watermarking— Attacks and countermeasures.Journal of Electronic Imaging, 10(3):825, 2001

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:52.968943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation edc06db8-b9e0-4871-875c-ce9f32388071 · outbound

This paper cites PyTorch: Pruning tutorial.https://docs.pytorch.

Energy Considerations for Large Pretrained Neural Networks PyTorch: Pruning tutorial.https://docs.pytorch

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:52.892682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3dcb5047-d26a-454e-b026-1df62cf36c32 · outbound

This paper cites To compress, or not to compress: Characterizing deep learn- ing model compression for embedded inference.

Energy Considerations for Large Pretrained Neural Networks To compress, or not to compress: Characterizing deep learn- ing model compression for embedded inference

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:52.825436Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4bae9c9-dcd5-4af0-9fde-006c0f0029ed · outbound

This paper cites Evaluation metrics and statis- tical tests for machine learning.Scientific Reports, 14(1):6086, 2024.

Energy Considerations for Large Pretrained Neural Networks Evaluation metrics and statis- tical tests for machine learning.Scientific Reports, 14(1):6086, 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:52.738025Z

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Observation 9a475b17-ef0d-425a-9505-fb28ecde00db · outbound

This paper cites an unresolved cited work.

Energy Considerations for Large Pretrained Neural Networks Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:50:52.619181Z

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Observation 75669d31-cb8f-4e24-8ec0-5cf1beeb3207 · outbound

This paper cites Sainath et al.

Energy Considerations for Large Pretrained Neural Networks Sainath et al

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:52.513470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7467eaed-ce02-4a9a-9b96-9dc205e109ba · outbound

This paper cites From words to watts: Benchmarking the energy costs of large language model inference.

Energy Considerations for Large Pretrained Neural Networks From words to watts: Benchmarking the energy costs of large language model inference

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:52.414334Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f35251f8-d8e3-4426-914e-f5783a868363 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

Energy Considerations for Large Pretrained Neural Networks Very deep convolutional networks for large-scale image recognition

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:52.298613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bc45de87-ebf0-47f7-ae03-076073693c91 · outbound

This paper cites Petitcolas Stefan Katzenbeisser.

Energy Considerations for Large Pretrained Neural Networks Petitcolas Stefan Katzenbeisser

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:52.205764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ca1fae3e-a31c-4254-9a4c-e47ce78a95e6 · outbound

This paper cites Going deeper with convolutions.

Energy Considerations for Large Pretrained Neural Networks Going deeper with convolutions

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:52.058508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e3ef9f54-1943-4328-9dad-f19bccde7a45 · outbound

This paper cites Thompson, Marc Schonwiesner, Yoshua Bengio, and Daniel Willett.

Energy Considerations for Large Pretrained Neural Networks Thompson, Marc Schonwiesner, Yoshua Bengio, and Daniel Willett

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:51.894089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 41d84e14-e364-4a8b-a5aa-a2e920b6b7b3 · outbound

This paper cites Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F.

Energy Considerations for Large Pretrained Neural Networks Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:51.789374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b2be8eda-12ce-456c-a440-f24ac1ee8545 · outbound

This paper cites The marginal value of adaptive gradient methods in machine learning.

Energy Considerations for Large Pretrained Neural Networks The marginal value of adaptive gradient methods in machine learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:51.705744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f74e6f7c-8aea-45a3-a97f-fe6fddf8fae1 · outbound

This paper cites Scaling for edge inference of deep neural networks.Nature Electronics, 1(4):216–222, 2018.

Energy Considerations for Large Pretrained Neural Networks Scaling for edge inference of deep neural networks.Nature Electronics, 1(4):216–222, 2018

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:51.504340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 094a5a3a-74fc-4e9d-9d16-20608e105d64 · outbound

This paper cites Quantization networks.

Energy Considerations for Large Pretrained Neural Networks Quantization networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:50:51.325352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 7b58af80-5e20-47ce-9a21-76200bb5697b · inbound

Concept Drift Detection and Adaptive Retraining of Malware Classification Models cites this paper.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Energy Considerations for Large Pretrained Neural Networks

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:31:49.662035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.