Paper Citation Record · LEDGER

Recommending Pre-Trained Models for IoT Devices

As of 14 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2412.18972.

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

pith.paper-citation-record.v1
2412.18972 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T01:02:46.536907Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

34 of 34 outbound references displayed

verified exact1
verified fuzzy30
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 05abfc87-b03b-4ea8-a5bb-c81910f92d1a · outbound

This paper cites A survey on IoT-based smart cars, their functionalities and challenges,.

Recommending Pre-Trained Models for IoT Devices A survey on IoT-based smart cars, their functionalities and challenges,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:08.184513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5bbe7f86-d455-427a-9b9e-e6cbe8244929 · outbound

This paper cites IoT based smart agriculture using machine learning,.

Recommending Pre-Trained Models for IoT Devices IoT based smart agriculture using machine learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:08.166958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 50217e53-aca7-4707-bc67-0b9b74d35376 · outbound

This paper cites V oice controlled home automation system using natural language processing (NLP) and internet of things (IoT),.

Recommending Pre-Trained Models for IoT Devices V oice controlled home automation system using natural language processing (NLP) and internet of things (IoT),

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:08.149836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 254ed18d-90a6-4eb4-a27c-899a7a8e2522 · outbound

This paper cites Smart at what cost? characterising mobile deep neural networks in the wild,.

Recommending Pre-Trained Models for IoT Devices Smart at what cost? characterising mobile deep neural networks in the wild,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T01:02:46.319224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bddfcb1-f5b6-4524-b676-02cbcf83b993 · outbound

This paper cites An empirical study of pre-trained model reuse in the hugging face deep learning model registry,.

Recommending Pre-Trained Models for IoT Devices An empirical study of pre-trained model reuse in the hugging face deep learning model registry,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:08.131805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f334e15a-63aa-4c66-a677-dfbf3c49a247 · outbound

This paper cites ”if security is required.

Recommending Pre-Trained Models for IoT Devices ”if security is required

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:08.114893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a7a2b33e-42fc-40dc-8439-81a10d427ea3 · outbound

This paper cites Transferability and hardness of supervised classification tasks,.

Recommending Pre-Trained Models for IoT Devices Transferability and hardness of supervised classification tasks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:08.097449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c41d8b83-d6de-49ca-bd10-ae0f9a7d1f0a · outbound

This paper cites An information-theoretic approach to transferability in task transfer learning,.

Recommending Pre-Trained Models for IoT Devices An information-theoretic approach to transferability in task transfer learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:08.078821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 06cc65e1-8101-45b8-bbd8-62cba7965ea8 · outbound

This paper cites LEEP: A new measure to evaluate transferability of learned representations,.

Recommending Pre-Trained Models for IoT Devices LEEP: A new measure to evaluate transferability of learned representations,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:08.061572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ce22e7b6-4cd0-4919-ba40-359abaebf2d5 · outbound

This paper cites Ranking neural checkpoints,.

Recommending Pre-Trained Models for IoT Devices Ranking neural checkpoints,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:08.044073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bf252216-e360-441c-80e1-04a2320a28d7 · outbound

This paper cites LogME: Practical assessment of pre-trained models for transfer learning,.

Recommending Pre-Trained Models for IoT Devices LogME: Practical assessment of pre-trained models for transfer learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:08.025496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d58225a8-64a8-4f6d-bf30-1f81643e2a19 · outbound

This paper cites PACTran: PAC-bayesian metrics for estimating the transferability of pretrained models to classification tasks,.

Recommending Pre-Trained Models for IoT Devices PACTran: PAC-bayesian metrics for estimating the transferability of pretrained models to classification tasks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:08.001834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8999c7a3-3efe-420c-b9d4-719293e561c0 · outbound

This paper cites Transferability estimation using bhattacharyya class separability,.

Recommending Pre-Trained Models for IoT Devices Transferability estimation using bhattacharyya class separability,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.960504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b58ff88f-f125-44de-85d0-66ff94fa9261 · outbound

This paper cites A linearized frame- work and a new benchmark for model selection for fine-tuning.

Recommending Pre-Trained Models for IoT Devices A linearized frame- work and a new benchmark for model selection for fine-tuning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.942406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 782c8d74-562c-4382-b027-de3ff30179f3 · outbound

This paper cites Model spider: Learning to rank pre-trained models efficiently,.

Recommending Pre-Trained Models for IoT Devices Model spider: Learning to rank pre-trained models efficiently,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.923724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 67f81a0a-082d-404e-9440-bd4937a16b70 · outbound

This paper cites Foundation model is efficient multimodal multitask model selector,.

Recommending Pre-Trained Models for IoT Devices Foundation model is efficient multimodal multitask model selector,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.905839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c63dda25-31df-49b2-8fb6-4d6a1a49544f · outbound

This paper cites Pre-trained model recommendation for downstream fine-tuning,.

Recommending Pre-Trained Models for IoT Devices Pre-trained model recommendation for downstream fine-tuning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.888381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2562934e-e4ba-432f-bc88-49d621724766 · outbound

This paper cites OTCE: A transferability metric for cross-domain cross-task representations,.

Recommending Pre-Trained Models for IoT Devices OTCE: A transferability metric for cross-domain cross-task representations,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.870852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a7384e5a-9a00-4055-8661-209da72e0a23 · outbound

This paper cites LwHBench: A low- level hardware component benchmark and dataset for single board computers,.

Recommending Pre-Trained Models for IoT Devices LwHBench: A low- level hardware component benchmark and dataset for single board computers,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.854421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1d81dc40-5db8-40f2-80c1-63acb447aed8 · outbound

This paper cites A comparative analysis for optimizing machine learning model deployment in IoT devices,.

Recommending Pre-Trained Models for IoT Devices A comparative analysis for optimizing machine learning model deployment in IoT devices,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.836294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2b957594-5688-4494-ae6a-efc29e8db980 · outbound

This paper cites Reusing deep learning models: Challenges and directions in software engineering,.

Recommending Pre-Trained Models for IoT Devices Reusing deep learning models: Challenges and directions in software engineering,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.817017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation de359458-d001-47fd-9557-ef8e1c021af8 · outbound

This paper cites Interoperability in deep learning: A user survey and failure analysis of onnx model converters,.

Recommending Pre-Trained Models for IoT Devices Interoperability in deep learning: A user survey and failure analysis of onnx model converters,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.795158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a43b87b8-1c90-4fe1-860d-d0cb6a21bae1 · outbound

This paper cites Challenges and practices of deep learning model reengineering: A case study on computer vision.

Recommending Pre-Trained Models for IoT Devices Challenges and practices of deep learning model reengineering: A case study on computer vision

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.775989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 650affb3-dbd6-4f1e-8750-c10ef7699291 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference,.

Recommending Pre-Trained Models for IoT Devices Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.757805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f23b9b3d-f5ce-4db6-b1e5-9308d0abbe8f · outbound

This paper cites Improving the interpretability of deep neural networks with knowledge distillation,.

Recommending Pre-Trained Models for IoT Devices Improving the interpretability of deep neural networks with knowledge distillation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.735262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 626e95d1-efc7-492f-a037-35f7f4d890f5 · outbound

This paper cites A reasonable social welfare function,.

Recommending Pre-Trained Models for IoT Devices A reasonable social welfare function,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.714498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c76c62c3-96e5-4de1-b222-03f18a0fc480 · outbound

This paper cites The copeland method: I.: Relationships and the dictionary,.

Recommending Pre-Trained Models for IoT Devices The copeland method: I.: Relationships and the dictionary,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.693584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 36683da9-91e7-4f71-8f05-091daefd5248 · outbound

This paper cites A Comprehensive Survey on Hardware-Aware Neural Architecture Search.

Recommending Pre-Trained Models for IoT Devices A Comprehensive Survey on Hardware-Aware Neural Architecture Search

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T01:02:46.496896Z

Source-reported events for the cited work

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Observation 6fd2b8e6-829d-4c41-9d97-b8f8a20a9882 · outbound

This paper cites MnasNet: Platform-Aware Neural Architecture Search for Mobile ,.

Recommending Pre-Trained Models for IoT Devices MnasNet: Platform-Aware Neural Architecture Search for Mobile ,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T01:02:46.505891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc67ec2b-bd88-4834-97a6-9a0020a95d50 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

Recommending Pre-Trained Models for IoT Devices Communication-efficient learning of deep networks from decentralized data,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.673122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 11217541-f833-4104-9f67-7bab3ab1ca6b · outbound

This paper cites Iot bugs and development challenges,.

Recommending Pre-Trained Models for IoT Devices Iot bugs and development challenges,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.653547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fcb245c5-0839-43d7-99b8-aacd09320471 · outbound

This paper cites A comprehensive study of autonomous vehicle bugs,.

Recommending Pre-Trained Models for IoT Devices A comprehensive study of autonomous vehicle bugs,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.634566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 80315b4b-3eee-4d3f-a3b4-20fd17088780 · outbound

This paper cites An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors.

Recommending Pre-Trained Models for IoT Devices An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-11T01:02:46.609725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0bc38ea7-a63b-45e9-a443-bbdf94766dbb · outbound

This paper cites 252–268, ISSN: 1611-3349.

Recommending Pre-Trained Models for IoT Devices 252–268, ISSN: 1611-3349

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T01:03:07.981473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.