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Paper Citation Record · LEDGER

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks

As of 19 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2505.15631.

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

pith.paper-citation-record.v1
2505.15631 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:18:52.415210Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

53 of 53 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation abf16a4b-0306-4a89-9930-5773d8e4a74f · outbound

This paper cites Gradient-based learning applied to document recognition,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Gradient-based learning applied to document recognition,

Reference 1

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Observation 48c87cf0-789c-414d-ade6-945375e2b3b0 · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Fully convolutional networks for semantic segmentation,

Reference 2

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Observation 839ea99b-ca3f-4166-8d4b-71593beb542f · outbound

This paper cites Long short-term memory,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Long short-term memory,

Reference 3

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Observation 0bda36c1-9255-40c6-aab4-0506b8062eda · outbound

This paper cites ImageNet classification with deep convolutional neural networks,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks ImageNet classification with deep convolutional neural networks,

Reference 4

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Observation 84b2c4f4-5b1a-4ab6-9073-2d22d0ecf9ad · outbound

This paper cites Going deeper with convolutions,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Going deeper with convolutions,

Reference 5

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Observation 9af8eb82-ac99-4e9a-b109-eefacf82a88a · outbound

This paper cites Deep residual learning for image recognition,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Deep residual learning for image recognition,

Reference 6

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Source-reported events for the cited work

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Observation 215fbffd-57ec-4700-a37f-2e32786eed20 · outbound

This paper cites Attention is all you need,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Attention is all you need,

Reference 7

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Observation f41897ec-4e63-4be8-be35-46d5256cacac · outbound

This paper cites Hutter, L.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Hutter, L

Reference 8

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation fdbe478a-e325-4565-a8af-d7f534f48e33 · outbound

This paper cites Neural architecture search: a survey,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Neural architecture search: a survey,

Reference 9

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Observation 6e6dd2c4-d45b-4cf9-8878-fa908ec36653 · outbound

This paper cites Meta pseudo labels,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Meta pseudo labels,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9dc50ddb-d0b7-4390-a6d6-f06e58369e5d · outbound

This paper cites Searching for efficient transformers for language modeling,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Searching for efficient transformers for language modeling,

Reference 11

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Observation 5824c7f4-f851-493f-b997-625473c86bb4 · outbound

This paper cites Neural architecture search with reinforcement learning,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Neural architecture search with reinforcement learning,

Reference 12

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Observation b1c863f8-e5d7-465f-874d-bc336f382cdc · outbound

This paper cites Green AI,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Green AI,

Reference 13

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Source-reported events for the cited work

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Observation 39abd4b1-387c-49dc-ba0f-466df3e64715 · outbound

This paper cites Towards Green Automated Machine Learning: Status Quo and Future Directions,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Towards Green Automated Machine Learning: Status Quo and Future Directions,

Reference 14

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Source-reported events for the cited work

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Observation c3f36c31-300c-4eba-9982-5e4ceca3cbc5 · outbound

This paper cites NAS-Bench-201: Extending the Scope of Repro- ducible Neural Architecture Search,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks NAS-Bench-201: Extending the Scope of Repro- ducible Neural Architecture Search,

Reference 15

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation fc1ce83d-6d3c-4a80-8c27-7dc9a468b99c · outbound

This paper cites Hardware-aware neural architecture search: Survey and taxonomy,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Hardware-aware neural architecture search: Survey and taxonomy,

Reference 16

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Observation 2cb4f4d5-78c8-4915-8668-aea87d8f1d0c · outbound

This paper cites Edge intelligence: Paving the last mile of artificial intelligence with edge computing,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Edge intelligence: Paving the last mile of artificial intelligence with edge computing,

Reference 17

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Source-reported events for the cited work

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Observation 3baad1fa-0778-4f3c-a951-75b42d4eed56 · outbound

This paper cites Learning iot in edge: Deep learning for the internet of things with edge computing,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Learning iot in edge: Deep learning for the internet of things with edge computing,

Reference 18

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Observation cc1fd5f9-c5b7-4e92-9eeb-4e0e29d7de00 · outbound

This paper cites HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark,

Reference 19

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Observation 467a1cbd-ab1a-455d-b2a5-a6c73b460bbc · outbound

This paper cites EA-HAS-bench: Energy-aware hyperparameter and architecture search benchmark,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks EA-HAS-bench: Energy-aware hyperparameter and architecture search benchmark,

Reference 20

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Observation 506d297b-601f-4e57-b67a-5fb0cd8d42ff · outbound

This paper cites NVIDIA Management Library,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks NVIDIA Management Library,

Reference 21

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Observation 477953d0-ef7b-4326-9d5f-64f8a5f8eda2 · outbound

This paper cites mlco2/codecarbon: v2.4.1,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks mlco2/codecarbon: v2.4.1,

Reference 22

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Observation adc8d06f-6996-4be2-9530-5e39aa95aacc · outbound

This paper cites Auto-pytorch tabular: Multi- fidelity metalearning for efficient and robust autodl,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Auto-pytorch tabular: Multi- fidelity metalearning for efficient and robust autodl,

Reference 23

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Observation 446f5adf-cdea-4d1b-8766-1c17d1410b07 · outbound

This paper cites Efficient neural architecture search via parameters sharing,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Efficient neural architecture search via parameters sharing,

Reference 24

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Observation 19ab99c1-e06f-4ee9-9aac-e468b0789096 · outbound

This paper cites Efficient multi-objective neural architecture search via lamarckian evolution,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Efficient multi-objective neural architecture search via lamarckian evolution,

Reference 25

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f69d9b5f-ad51-40a3-a9ca-7172aa179656 · outbound

This paper cites Large-scale evolution of image classifiers,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Large-scale evolution of image classifiers,

Reference 26

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 0721a9c7-010c-4c94-994b-8f474f830b0a · outbound

This paper cites DARTS: Differentiable Architecture Search,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks DARTS: Differentiable Architecture Search,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 23a7f376-ed35-48fe-8484-a96c9bd71569 · outbound

This paper cites DARTS-: Robustly Stepping out of Performance Collapse Without Indicators,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks DARTS-: Robustly Stepping out of Performance Collapse Without Indicators,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation e797d998-6c5f-4299-8822-95b48ed11ad0 · outbound

This paper cites Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search,

Reference 29

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation eca04ef9-d07c-4a50-b7fa-ecd6130ae7f7 · outbound

This paper cites Progressive differentiable architecture search: Bridging the depth gap between search and evalua- tion,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Progressive differentiable architecture search: Bridging the depth gap between search and evalua- tion,

Reference 30

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9fe13056-a94e-40c6-9925-107d9b61358b · outbound

This paper cites Understanding and robustifying differentiable architecture search,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Understanding and robustifying differentiable architecture search,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation e24a9f80-d9d0-4abe-88d8-f9fcacd982f8 · outbound

This paper cites Zero-shot neural architecture search: Challenges, solutions, and opportunities,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Zero-shot neural architecture search: Challenges, solutions, and opportunities,

Reference 32

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raw_fallback, observed 2026-08-07T15:18:57.103557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c357306e-41fa-41f0-aca0-f50587c962fe · outbound

This paper cites MicroNAS: Zero-Shot Neural Architecture Search for MCUs,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks MicroNAS: Zero-Shot Neural Architecture Search for MCUs,

Reference 33

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raw_fallback, observed 2026-08-07T15:18:56.929414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:50.421388Z digest=sha256:7071524b5931046af8e717cf89a930694fa6b590c819c9dd01cef86d765f6ed1

Observation a2e33cfd-26a5-42bf-8164-46cfd9e44b9b · outbound

This paper cites An efficient multi-objective evolutionary zero-shot neural architecture search framework for image classification,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks An efficient multi-objective evolutionary zero-shot neural architecture search framework for image classification,

Reference 34

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raw_fallback, observed 2026-08-07T15:18:56.764259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:50.527713Z digest=sha256:c673cbe17778d3419a78532432a38fcc7748380b5859a08dfe73b306bee23e9d

Observation 2ced9483-3340-415b-8141-081c9802b996 · outbound

This paper cites NAS-Bench-101: Towards reproducible neural architecture search,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks NAS-Bench-101: Towards reproducible neural architecture search,

Reference 35

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raw_fallback, observed 2026-08-07T15:18:56.550828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:50.606414Z digest=sha256:1e5653b068d78e936ece274826150e035eb80b17da1f178a4679433b796bfbe5

Observation be05c56c-ecab-4985-b02d-3504d140aef8 · outbound

This paper cites NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search,

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-07T15:18:56.294510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:50.658865Z digest=sha256:002618bab46f714f08321abf62034e2bd9beb6311e01f5536ea7a0a22993a5c4

Observation df856f49-63cd-4005-9779-7ae618cbac89 · outbound

This paper cites JAHS- Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks JAHS- Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search,

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-07T15:18:56.045021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:50.749979Z digest=sha256:428841c5e77c3a936ae8d9434c4518a50968b53614da19d4764c9f770f5daf2c

Observation 490692bb-9e1f-4cc0-918e-167877517191 · outbound

This paper cites EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search,

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-07T15:18:55.769843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:50.843060Z digest=sha256:45a1beaae57cb1abab54cfd8a12d240f8c9a4caf85a9ed56db0259177ae33cba

Observation 448a8a22-ab5d-44ee-87cf-efd9d37c2778 · outbound

This paper cites Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators,

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-07T15:18:55.604642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:50.966408Z digest=sha256:577c44743fcfc0bfe0fb1c7e622ce53aab684fdc4dccc9eee1be1564e88701d9

Observation 53776d76-29cf-4180-bef6-0928c3827fc4 · outbound

This paper cites Accurate and convenient energy measurements for gpus: A detailed study of nvidia gpu’s built-in power sensor,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Accurate and convenient energy measurements for gpus: A detailed study of nvidia gpu’s built-in power sensor,

Reference 40

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verified fuzzy
raw_fallback, observed 2026-08-07T15:18:55.351869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:51.052053Z digest=sha256:4efab5c92f6c4ca15b864ca67621e364d6570fbf91f049c26c0839731d9ec5e0

Observation 07ef6c8b-ea45-4712-a5e0-b02c3fb20596 · outbound

This paper cites NVIDIA System Management Interface Documentation,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks NVIDIA System Management Interface Documentation,

Reference 41

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verified fuzzy
raw_fallback, observed 2026-08-07T15:18:55.117893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:51.164926Z digest=sha256:0a133db2bd3b3dd03140ce185960b658889b7b23a44b453fb805c320334d9333

Observation a100df48-257f-4170-a34c-98cf9441c3b6 · outbound

This paper cites RAPL: memory power estimation and capping,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks RAPL: memory power estimation and capping,

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-07T15:18:54.851015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:51.239150Z digest=sha256:ddc5f089154e5a3cb084e8e1b60aea78acbac8a6ef42c14537ff8d257a527131

Observation 28ab4cf2-0171-42fb-bae8-935390d33d89 · outbound

This paper cites Rouvoy, “pyRAPL,” 2023.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Rouvoy, “pyRAPL,” 2023

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-07T15:18:54.566043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:51.331860Z digest=sha256:2ba648c6463f7e185f91ff206c83b9dcf6a06a347e79da7c5a4fe1d6b92e2b99

Observation 1efbb96b-0601-4c92-9e3f-89567d9bf9d3 · outbound

This paper cites Power monitoring with papi for extreme scale architectures and dataflow-based program- ming models,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Power monitoring with papi for extreme scale architectures and dataflow-based program- ming models,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:18:53.948196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:51.413624Z digest=sha256:fbb320b3c316dc583cda62834f5068de4c71cbe399fc8882cdf1504de8036012

Observation db2ae559-1a89-4010-8abe-c1aa1a3d2877 · outbound

This paper cites LIKWID: A Lightweight Performance-Oriented Tool Suite for x86 Multicore Environments,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks LIKWID: A Lightweight Performance-Oriented Tool Suite for x86 Multicore Environments,

Reference 45

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verified fuzzy
raw_fallback, observed 2026-08-07T15:18:53.459320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:51.517994Z digest=sha256:d77d78a8cb669a0069659b7fb03dce4bfb34c9c1494a40346842239aa5013bba

Observation db84d1a1-725a-4f09-889d-5daeb6161c0c · outbound

This paper cites A Validation of DRAM RAPL Power Measurements,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks A Validation of DRAM RAPL Power Measurements,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:18:53.254563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:51.633683Z digest=sha256:ad6012a6b01c94102717c41dfd56fa7f3598981641f823befa1835de81bcf098

Observation 4ba96c74-643f-444e-a067-d11638791360 · outbound

This paper cites Quantifying the Carbon Emissions of Machine Learning.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Quantifying the Carbon Emissions of Machine Learning

Reference 47

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unresolved
no resolver link, observed 2026-08-07T15:18:51.687157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:18:51.687157Z digest=sha256:1c4e1dbfa40b0023d02cfba07a3cb1104dbe663a866c52ee68b2896c80e8c71e

Observation b8b94a0f-ba3f-40f7-86af-24a9f80dc92a · outbound

This paper cites Energy Usage Reports: Environmental awareness as part of algorithmic accountability.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Energy Usage Reports: Environmental awareness as part of algorithmic accountability

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T15:18:51.835109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:18:51.835109Z digest=sha256:72008b964f0c76126297a0ba297056971f0f3098ef905a668922eb8232097bb3

Observation a9a9c26e-cc86-4aaa-ac23-8fa133c307a9 · outbound

This paper cites How to estimate carbon footprint when training deep learning models? A guide and review.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks How to estimate carbon footprint when training deep learning models? A guide and review

Reference 49

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:18:52.575245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:51.952817Z digest=sha256:033315b925c19fc2019fe8e9615d6e4197402ee056ce9ab583296cf4807a6c9c

Observation 87b38b38-1b44-4c80-b892-bcc29ecd900a · outbound

This paper cites The carbon footprint of machine learning training will plateau, then shrink,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks The carbon footprint of machine learning training will plateau, then shrink,

Reference 50

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unresolved
no resolver link, observed 2026-08-07T15:18:52.033327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:18:52.033327Z digest=sha256:ad4b4ea3ede59820e59a5c472909a6f515dce2f0faedd366b5b7fbfe22178f31

Observation 094f26a2-9bd0-4711-a14f-b02c12f215a1 · outbound

This paper cites Introducing FIRESTARTER: A processor stress test utility,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks Introducing FIRESTARTER: A processor stress test utility,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:18:52.993659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:52.158089Z digest=sha256:6db6f9bab21e01b72975a844787040a52d692b53be8ca369f0ab7ee432697cd1

Observation 50760b4d-e145-44b3-b333-1cc4e5bcc124 · outbound

This paper cites RegNet: Self- Regulated Network for Image Classification,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks RegNet: Self- Regulated Network for Image Classification,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T15:18:52.822611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T15:18:52.308530Z digest=sha256:e0b7b240cf048b3a528a0500a53eef870959623420c865e8c00d2bcf882c358d

Observation 06a1eb46-053c-4b1b-906e-e487c0fd09b6 · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge,.

Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks ImageNet Large Scale Visual Recognition Challenge,

Reference 53

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unresolved
no resolver link, observed 2026-08-07T15:18:52.415210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:18:52.415210Z digest=sha256:3bd488a4de853f94dff083ae69a1b6a346dbb2511dad276a74cc1cd42e524fe0

Pith citing papers

No inbound Pith citation observations are available.