Paper Citation Record · LEDGER

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks

As of 19 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.07411.

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

pith.paper-citation-record.v1
2505.07411 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:22:58.262373Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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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

41 of 41 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 96766cd7-39e1-4b71-81b7-bcc56fb9415b · outbound

This paper cites DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks DLAS: A Conceptual Model for Across-Stack Deep Learning Acceleration,

Reference 2

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Observation 126df150-6f61-43d7-9a25-a1b0229b0e1f · outbound

This paper cites Linearly Replaceable Filters for Deep Network Channel Pruning,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Linearly Replaceable Filters for Deep Network Channel Pruning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.777153Z

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Observation f22195c0-3d7f-4051-92b8-c1534fd67bbb · outbound

This paper cites An Entropy-based Pruning Method for CNN Compression.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks An Entropy-based Pruning Method for CNN Compression

Reference 4

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Observation 89885d7d-138d-49d0-a3a0-8f0bbd5342a0 · outbound

This paper cites Thinet: A filter level pruning method for deep neural network compression,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Thinet: A filter level pruning method for deep neural network compression,

Reference 5

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Observation 709dc15d-2d4b-47bb-8082-badcd8a42dd4 · outbound

This paper cites Pruning filters for efficient convnets,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Pruning filters for efficient convnets,

Reference 6

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Observation 5164eed0-069d-49aa-96a4-317612e6a2ad · outbound

This paper cites Automatic attention pruning: Improv- ing and automating model pruning using attentions,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Automatic attention pruning: Improv- ing and automating model pruning using attentions,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.735060Z

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Observation 5be34f93-3d77-4770-8609-0659db216112 · outbound

This paper cites Pruning convolutional neural networks for resource efficient inference,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Pruning convolutional neural networks for resource efficient inference,

Reference 8

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Observation 2f8b06a0-1b55-4b81-acb3-d6e2822cae15 · outbound

This paper cites What is the State of Neural Network Pruning?.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks What is the State of Neural Network Pruning?

Reference 9

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Observation d5315563-37cc-4208-8e65-687b8df97396 · outbound

This paper cites Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.707417Z

Source-reported events for the cited work

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Observation 41e3454a-b24a-4e3e-8dc5-bb7a9a52022a · outbound

This paper cites Model compression and hardware acceleration for neural networks: A comprehensive survey,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Model compression and hardware acceleration for neural networks: A comprehensive survey,

Reference 11

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Observation 1e5f22f9-e503-44d2-9684-2f50844b1389 · outbound

This paper cites Soft filter pruning for accelerating deep convolutional neural networks,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Soft filter pruning for accelerating deep convolutional neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.696259Z

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Observation 06284ee6-2096-4598-850f-d1f28a1c69d8 · outbound

This paper cites Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.684423Z

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Observation a32812fd-7d9b-4e94-8ba2-b2de2c2acff9 · outbound

This paper cites Are Sixteen Heads Really Better than One?.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Are Sixteen Heads Really Better than One?

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.671135Z

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Observation 376d103a-cab4-435a-a512-684d1856a21c · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Pruning Convolutional Neural Networks for Resource Efficient Inference,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.657662Z

Source-reported events for the cited work

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Observation a7e93adb-16a2-4b51-a021-6a37b0b00e37 · outbound

This paper cites ICE-pick: Iterative cost-efficient pruning for DNNs,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks ICE-pick: Iterative cost-efficient pruning for DNNs,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.645892Z

Source-reported events for the cited work

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Observation a47a325d-d910-4324-82cc-1650b7484d3b · outbound

This paper cites Nfp: A no fine- tuning pruning approach for convolutional neural network compression,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Nfp: A no fine- tuning pruning approach for convolutional neural network compression,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.633633Z

Source-reported events for the cited work

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Observation 50f5675e-ca9e-4cf0-a4a3-4bf9eca7fed6 · outbound

This paper cites Pruning filters while training for efficiently optimizing deep learning networks,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Pruning filters while training for efficiently optimizing deep learning networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.619622Z

Source-reported events for the cited work

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Observation e23f103f-f663-4859-b98d-7e1f6c071b07 · outbound

This paper cites FreezeOut: Accelerate Training by Progressively Freezing Layers,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks FreezeOut: Accelerate Training by Progressively Freezing Layers,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.603673Z

Source-reported events for the cited work

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Observation 011d898f-4d48-41a3-81c3-961a37d00674 · outbound

This paper cites AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning

Reference 20

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Observation b90bb6cd-fe5a-47bd-9b84-0c05da99f771 · outbound

This paper cites What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning

Reference 21

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Observation ee443bbf-35e1-418b-8910-31d329d47fd7 · outbound

This paper cites Local Masking Meets Progressive Freezing: Crafting Efficient Vision Transformers for Self-Supervised Learning,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Local Masking Meets Progressive Freezing: Crafting Efficient Vision Transformers for Self-Supervised Learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.587642Z

Source-reported events for the cited work

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Observation 5afbf0f0-c583-495a-9865-473b62309742 · outbound

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

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.572900Z

Source-reported events for the cited work

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Observation 261945ca-8eea-470b-98c7-2eda2dd974c0 · outbound

This paper cites Do we actually need dense over-parameterization? in-time over-parameterization in sparse training,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Do we actually need dense over-parameterization? in-time over-parameterization in sparse training,

Reference 24

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Observation a5e18390-96b8-4400-8ebc-92dd520fec53 · outbound

This paper cites Towards explaining the regularization effect of initial large learning rate in training neural networks,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Towards explaining the regularization effect of initial large learning rate in training neural networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.541111Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0c33ed33-5a35-4259-992e-2781988e34ee · outbound

This paper cites On the Variance of the Adaptive Learning Rate and Beyond,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks On the Variance of the Adaptive Learning Rate and Beyond,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.529092Z

Source-reported events for the cited work

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Observation 9f37eab4-8cf1-4d63-af2f-f81ff56abe2c · outbound

This paper cites Visualizing the loss landscape of neural nets,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Visualizing the loss landscape of neural nets,

Reference 27

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Observation 75856f42-f771-48c5-99f3-8f68be0a0ae8 · outbound

This paper cites S-Cyc: A Learning Rate Schedule for Iterative Pruning of ReLU-based Networks.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks S-Cyc: A Learning Rate Schedule for Iterative Pruning of ReLU-based Networks

Reference 28

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Source-reported events for the cited work

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Observation 43b13feb-bc5d-4d5b-bda4-b05ac0b37ded · outbound

This paper cites Algorithms for hyper- parameter optimization,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Algorithms for hyper- parameter optimization,

Reference 29

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Source-reported events for the cited work

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Observation de7d5608-f18e-45ef-abf6-7113138e0742 · outbound

This paper cites Optuna: A Next-generation Hyperparameter Optimization Framework,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Optuna: A Next-generation Hyperparameter Optimization Framework,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:22:58.496209Z

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Observation 7f70f34d-c0dc-4219-87ff-cc1edcc411e7 · outbound

This paper cites Learning Multiple Layers of Features from Tiny Im- ages,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Learning Multiple Layers of Features from Tiny Im- ages,

Reference 31

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Observation fad4d535-12cf-496b-b0d7-af2078fa4c9e · outbound

This paper cites Tiny ImageNet Visual Recognition Challenge,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Tiny ImageNet Visual Recognition Challenge,

Reference 32

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Observation cb15bd4c-adfc-44a1-9dae-3675cd0d6549 · outbound

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

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Imagenet: A large-scale hierarchical image database,

Reference 33

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Source-reported events for the cited work

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Observation e60ba28c-4787-4984-835f-cf129c276c3b · outbound

This paper cites Distilling the Knowledge in a Neural Network.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Distilling the Knowledge in a Neural Network

Reference 34

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Observation b2d8fed3-72f7-47ff-bcaf-a2107b1da6eb · outbound

This paper cites Distilling with Performance Enhanced Students.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Distilling with Performance Enhanced Students

Reference 35

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Observation dda6e8c2-206c-4130-8305-1cd294b0f38a · outbound

This paper cites Deep Residual Learning for Image Recognition,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Deep Residual Learning for Image Recognition,

Reference 36

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Observation f37db7dd-f984-4f9f-a7bd-05ee12ec61ee · outbound

This paper cites Densely connected convolutional networks,.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Densely connected convolutional networks,

Reference 37

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Observation 8163fc16-3ab5-46af-8b47-7abbf4155583 · outbound

This paper cites Wide Residual Networks.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Wide Residual Networks

Reference 38

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Observation 1797b787-1d8c-4a1a-9bd2-445656cc49d0 · outbound

This paper cites An overview of gradient descent optimization algorithms.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks An overview of gradient descent optimization algorithms

Reference 39

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Observation 3792ca1e-9e3a-4dc2-991a-1bef4a6fd47b · outbound

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

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks Pytorch: An imperative style, high-performance deep learning library,

Reference 40

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Observation 48333c4c-c0b5-46df-93e8-d3a10eabb326 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T22:22:58.258538Z

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Observation e3c5d2d6-3b27-46a8-8b05-2850d781e743 · outbound

This paper cites A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models.

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

Reference 42

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Pith citing papers

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