Paper Citation Record · LEDGER

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures

As of 21 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2505.11569.

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

pith.paper-citation-record.v1
2505.11569 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:02:18.190862Z

measured 28 of 28 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 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 exact2
verified fuzzy10
unresolved16
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d819d4c4-a71c-4275-a6f9-823ec0033981 · outbound

This paper cites write newline.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures write newline

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:18.751968Z

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 c0d319f7-f30d-40e8-b7fb-aaf7e9680181 · outbound

This paper cites write newline.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures write newline

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:18.739350Z

Source-reported events for the cited work

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Observation aea131a3-cb95-4780-9cde-57b7b2d12eb6 · outbound

This paper cites write newline.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures write newline

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:18.723633Z

Source-reported events for the cited work

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Observation ac44420c-79d3-4e65-a131-8739e863a366 · outbound

This paper cites write newline.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures write newline

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:18.707722Z

Source-reported events for the cited work

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Observation 85b1ad32-371e-48ea-9372-d3e407667b4b · outbound

This paper cites write newline.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures write newline

Reference 5

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Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures , " * write output.state after.block = add.period write newline

Reference 6

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Observation 36bf25da-d4dd-408d-b4ef-823c46dc4a4a · outbound

This paper cites write newline.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures write newline

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:18.666817Z

Source-reported events for the cited work

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Observation 1432182b-5fb8-4ec6-b977-4a26f2840ebd · outbound

This paper cites @esa ( ) , n @biblabelnum##1 ##1.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures @esa ( ) , n @biblabelnum##1 ##1

Reference 8

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Observation d85aff0c-e802-4b43-98f6-f4dd30e36088 · outbound

This paper cites an unresolved cited work.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Unresolved cited work

Reference 9

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Source-reported events for the cited work

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Observation d247b337-9fad-4aea-b056-d5f326a3d92f · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Pruning Filters for Efficient ConvNets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:18.108723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c658981-5e79-4a23-9637-ec8abc3b6f82 · outbound

This paper cites DepGraph: Towards Any Structural Pruning.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures DepGraph: Towards Any Structural Pruning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:18.114062Z

Source-reported events for the cited work

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Observation 954a650a-882a-4a62-94a1-8192dd6c1c19 · outbound

This paper cites SteppingNet: A Stepping Neural Network with Incremental Accuracy Enhancement.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures SteppingNet: A Stepping Neural Network with Incremental Accuracy Enhancement

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:02:18.531269Z

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 f9209497-cafd-40d1-bf95-d03014ca7ad6 · outbound

This paper cites http://dx.doi.org/10.1145/3241539.3241559 NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures http://dx.doi.org/10.1145/3241539.3241559 NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:18.123746Z

Source-reported events for the cited work

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Observation b46391f9-4caa-4865-8757-a10148322915 · outbound

This paper cites an unresolved cited work.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:18.127835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0028a13f-8c10-4e08-bd10-69b8fc7038f1 · outbound

This paper cites Slimmable Neural Networks.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Slimmable Neural Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:18.131878Z

Source-reported events for the cited work

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Observation 5e868479-0f9f-494c-a297-fe8114f03c6e · outbound

This paper cites Torch-Pruning: An Open-Source Library for Structured Pruning in PyTorch.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Torch-Pruning: An Open-Source Library for Structured Pruning in PyTorch

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:18.631350Z

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 7be0b92b-7f7c-4c08-bee4-3828aa716dd1 · outbound

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

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99596822-34c3-4824-ac5f-0159820bd4fb · outbound

This paper cites Importance Estimation for Neural Network Pruning.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Importance Estimation for Neural Network Pruning

Reference 19

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Source-reported events for the cited work

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Observation c4636798-48c4-450c-8bcc-b7ce8dced4e0 · outbound

This paper cites Learning Efficient Convolutional Networks through Network Slimming.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Learning Efficient Convolutional Networks through Network Slimming

Reference 20

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Source-reported events for the cited work

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Observation 2f08e9cb-49bb-47e7-976c-46c787245c9b · outbound

This paper cites Learning Structured Sparsity in Deep Neural Networks.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Learning Structured Sparsity in Deep Neural Networks

Reference 21

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Source-reported events for the cited work

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Observation 6e3c61e1-eaee-4cec-9329-34371b1c996c · outbound

This paper cites Once-for-All: Train One Network and Specialize it for Efficient Deployment.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:18.160362Z

Source-reported events for the cited work

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Observation 64eb06ae-2ccf-4a21-96a1-bba91e3d3a91 · outbound

This paper cites Universally Slimmable Networks and Improved Training Techniques.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Universally Slimmable Networks and Improved Training Techniques

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbeb24d3-19ab-4b25-9527-f64778902559 · outbound

This paper cites MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:02:18.271352Z

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 56868771-abe8-48ce-959a-6a6b7f9f067e · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:18.174346Z

Source-reported events for the cited work

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Observation a63814d1-12a9-45d2-8459-b5b7271b9bf0 · outbound

This paper cites Snellius.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Snellius

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:18.615674Z

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 170bc358-7fb4-4613-938a-83d25b607d2e · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T21:02:18.182583Z

Source-reported events for the cited work

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Observation 83c1887b-e29b-42db-8f26-b4da18c71a2b · outbound

This paper cites https://github.com/Lightning-AI/lightning PyTorch Lightning , March 2019.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures https://github.com/Lightning-AI/lightning PyTorch Lightning , March 2019

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:18.600030Z

Source-reported events for the cited work

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Observation bec8c9ba-9bdf-440d-9dac-e2a02a0fb528 · outbound

This paper cites https://www.wandb.com/ Experiment Tracking with Weights and Biases , 2020.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures https://www.wandb.com/ Experiment Tracking with Weights and Biases , 2020

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:02:18.585700Z

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

No inbound Pith citation observations are available.