Paper Citation Record · LEDGER

Electrostatic Force Regularization for Neural Structured Pruning

As of 15 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2411.11079.

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

pith.paper-citation-record.v1
2411.11079 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:02:07.166109Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:50:30.719164Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T22:49:40.526907Z

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy25
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 16ed0cad-f0e3-4585-a503-3a489ddc7227 · outbound

This paper cites Restructuring the teacher and student in self-distillation.

Electrostatic Force Regularization for Neural Structured Pruning Restructuring the teacher and student in self-distillation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.620401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8b1b10c3-82d7-4a2d-a5e4-00a7ad095fb0 · outbound

This paper cites Low-rank approximation for sparse attention in multi-modal llms.

Electrostatic Force Regularization for Neural Structured Pruning Low-rank approximation for sparse attention in multi-modal llms

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.608859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation be6bcba9-de59-4ba5-9ded-b4a7d72cab78 · outbound

This paper cites Quantization via distillation and contrastive learning.

Electrostatic Force Regularization for Neural Structured Pruning Quantization via distillation and contrastive learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.023464Z

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Observation 9f5e2f25-15ae-408b-a024-1c98bdfa4f12 · outbound

This paper cites Discrimination-aware network pruning for deep model compression.

Electrostatic Force Regularization for Neural Structured Pruning Discrimination-aware network pruning for deep model compression

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.589674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8d51df25-c577-48c6-b83d-04b13658e9f2 · outbound

This paper cites Complexity-driven model compression for resource- constrained deep learning on edge.

Electrostatic Force Regularization for Neural Structured Pruning Complexity-driven model compression for resource- constrained deep learning on edge

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.577800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 23a2a498-784f-431f-9221-fd4af53e62d6 · outbound

This paper cites Ganji, Ivan Lazarevich, and Sudhakar Sah.

Electrostatic Force Regularization for Neural Structured Pruning Ganji, Ivan Lazarevich, and Sudhakar Sah

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.564928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 256ca393-65a0-412f-b1d6-1a3e54b521f6 · outbound

This paper cites Advancing model pruning via bi-level optimization.

Electrostatic Force Regularization for Neural Structured Pruning Advancing model pruning via bi-level optimization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.553358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 96f148d5-3ebe-4bd1-9c52-0d43edfc7a45 · outbound

This paper cites Prior gradient mask guided pruning-aware fine-tuning.

Electrostatic Force Regularization for Neural Structured Pruning Prior gradient mask guided pruning-aware fine-tuning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.541794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a91118c7-212b-4ef2-a02a-02223f8223c4 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks.

Electrostatic Force Regularization for Neural Structured Pruning The lottery ticket hypothesis: Finding sparse, trainable neural networks

Reference 9

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Source-reported events for the cited work

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Observation 8d25048f-4829-4109-a7ac-82c7055a005b · outbound

This paper cites Rethinking the Value of Network Pruning.

Electrostatic Force Regularization for Neural Structured Pruning Rethinking the Value of Network Pruning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.051049Z

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Observation 07721258-f0f8-4a1a-87c8-d32ad57e26b9 · outbound

This paper cites Snip: Single-shot network pruning based on connection sensitivity.

Electrostatic Force Regularization for Neural Structured Pruning Snip: Single-shot network pruning based on connection sensitivity

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.522977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 61b6a824-5347-44f4-8b7a-67f40e9f968e · outbound

This paper cites Progressive skeletonization: Trimming more fat from a network at initialization.

Electrostatic Force Regularization for Neural Structured Pruning Progressive skeletonization: Trimming more fat from a network at initialization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.511201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 31c0cf6e-1ca9-458f-8a91-ca2b8cbc455b · outbound

This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

Electrostatic Force Regularization for Neural Structured Pruning Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.063054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59e6af9b-9918-45d4-8345-1c855ef25bc1 · outbound

This paper cites Linear mode connectivity and the lottery ticket hypothesis.

Electrostatic Force Regularization for Neural Structured Pruning Linear mode connectivity and the lottery ticket hypothesis

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.067262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 955a63d9-cbe0-47c5-8949-b4060143dcea · outbound

This paper cites Neural pruning via growing regularization.

Electrostatic Force Regularization for Neural Structured Pruning Neural pruning via growing regularization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.491914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9138e827-757e-411a-b0f7-869c37158020 · outbound

This paper cites Pruning parameterization with bi-level optimization for efficient semantic segmentation on the edge.

Electrostatic Force Regularization for Neural Structured Pruning Pruning parameterization with bi-level optimization for efficient semantic segmentation on the edge

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.479074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9a13e049-2d32-4c6d-97bc-9a26644b5ee3 · outbound

This paper cites Gradual channel pruning while training using feature relevance scores for convolutional neural networks.

Electrostatic Force Regularization for Neural Structured Pruning Gradual channel pruning while training using feature relevance scores for convolutional neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.465925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bf496c94-854b-4cc3-83a6-5be4adf5545e · outbound

This paper cites Learning filter pruning criteria for deep convolutional neural networks acceleration.

Electrostatic Force Regularization for Neural Structured Pruning Learning filter pruning criteria for deep convolutional neural networks acceleration

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.454417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 84302508-a3c5-4715-b913-07407cb8c322 · outbound

This paper cites Structured compression of deep neural networks with debiased elastic group lasso.

Electrostatic Force Regularization for Neural Structured Pruning Structured compression of deep neural networks with debiased elastic group lasso

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.442505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 275f7a2b-df6b-496b-be21-4f908676f5f7 · outbound

This paper cites Rigging the lottery: Making all tickets winners.

Electrostatic Force Regularization for Neural Structured Pruning Rigging the lottery: Making all tickets winners

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.430976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 77ca09c2-a855-41b9-bec3-bb577a89cc69 · outbound

This paper cites Efficient joint optimization of layer-adaptive weight pruning in deep neural networks.

Electrostatic Force Regularization for Neural Structured Pruning Efficient joint optimization of layer-adaptive weight pruning in deep neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.418046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1a4343ef-5355-46b5-932d-fc9ef31b1735 · outbound

This paper cites Torque based structured pruning for deep neural network.

Electrostatic Force Regularization for Neural Structured Pruning Torque based structured pruning for deep neural network

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.405225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bbf59f8a-f2cb-47a6-a71d-3480457f2d6e · outbound

This paper cites Orthcaps: An orthogonal capsnet with sparse attention routing and pruning.

Electrostatic Force Regularization for Neural Structured Pruning Orthcaps: An orthogonal capsnet with sparse attention routing and pruning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.393765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ea012846-d7e6-4a02-99ea-5e7e2e7edf67 · outbound

This paper cites Finding lottery tickets in vision models via data- driven spectral foresight pruning.

Electrostatic Force Regularization for Neural Structured Pruning Finding lottery tickets in vision models via data- driven spectral foresight pruning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.381317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0d6bb1a0-4318-4073-82be-5236cfcde785 · outbound

This paper cites Bilevelpruning: Unified dynamic and static channel pruning for convolutional neural networks.

Electrostatic Force Regularization for Neural Structured Pruning Bilevelpruning: Unified dynamic and static channel pruning for convolutional neural networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.367307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3b9b482a-ab01-4e8e-9ed2-1174cdf54ad3 · outbound

This paper cites Unipts: A unified framework for proficient post-training sparsity.

Electrostatic Force Regularization for Neural Structured Pruning Unipts: A unified framework for proficient post-training sparsity

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.355604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5a79af07-95d2-438e-8732-ebad2a3b1c33 · outbound

This paper cites Channel pruning for accelerating very deep neural networks.

Electrostatic Force Regularization for Neural Structured Pruning Channel pruning for accelerating very deep neural networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.121781Z

Source-reported events for the cited work

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Observation 200a1456-21db-4a8f-b72b-a239d16e47e8 · outbound

This paper cites Amc: Automl for model compression and acceleration on mobile devices.

Electrostatic Force Regularization for Neural Structured Pruning Amc: Automl for model compression and acceleration on mobile devices

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.126407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9b66c7a-6e2b-4dc2-acd9-51136fd37aa7 · outbound

This paper cites Filter pruning via geometric median for deep convolutional neural networks acceleration.

Electrostatic Force Regularization for Neural Structured Pruning Filter pruning via geometric median for deep convolutional neural networks acceleration

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.130472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a9bad41-f41b-411b-8722-0fff08a63d7b · outbound

This paper cites Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks.

Electrostatic Force Regularization for Neural Structured Pruning Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.134271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8fd5dcd-e2f3-4b15-8fad-7c0ef02075ac · outbound

This paper cites Whc: Weighted hybrid criterion for filter pruning on convolutional neural networks.

Electrostatic Force Regularization for Neural Structured Pruning Whc: Weighted hybrid criterion for filter pruning on convolutional neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.319746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 57597a50-0700-4e9a-8c51-d49b8546f432 · outbound

This paper cites Channel Pruning via Automatic Structure Search.

Electrostatic Force Regularization for Neural Structured Pruning Channel Pruning via Automatic Structure Search

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.142131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9632a792-7fb4-4425-abea-55463ba8ae20 · outbound

This paper cites Channel pruning via lookahead search guided reinforcement learning.

Electrostatic Force Regularization for Neural Structured Pruning Channel pruning via lookahead search guided reinforcement learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.304662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b81d3942-1b7d-45d7-9840-8a8d23798dc3 · outbound

This paper cites Centripetal sgd for pruning very deep convolu- tional networks with complicated structure.

Electrostatic Force Regularization for Neural Structured Pruning Centripetal sgd for pruning very deep convolu- tional networks with complicated structure

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.291963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f4be1a59-d48d-4057-9503-b9c0e2136f7d · outbound

This paper cites Auto-balanced filter pruning for efficient convolutional neural networks.

Electrostatic Force Regularization for Neural Structured Pruning Auto-balanced filter pruning for efficient convolutional neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:02:07.277319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cdd55650-3f83-495b-9fdc-4bd1c91278ab · outbound

This paper cites Eigendamage: Structured pruning in the kronecker-factored eigenbasis.

Electrostatic Force Regularization for Neural Structured Pruning Eigendamage: Structured pruning in the kronecker-factored eigenbasis

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.157307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 872151f5-fe68-4781-bea8-cdbed99cb318 · outbound

This paper cites Importance estimation for neural network pruning.

Electrostatic Force Regularization for Neural Structured Pruning Importance estimation for neural network pruning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.162268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b484c8c-38fa-4b22-b812-e475426aa591 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Electrostatic Force Regularization for Neural Structured Pruning Pruning Filters for Efficient ConvNets

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.166109Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation c26a3c3f-8dcd-4936-baf0-f88fe9b2dd14 · inbound

Deep Convolutional Neural Networks Structured Pruning via Gravity Regularization cites this paper.

Deep Convolutional Neural Networks Structured Pruning via Gravity Regularization Electrostatic Force Regularization for Neural Structured Pruning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T12:50:30.719164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80a543d7-1bae-4c28-b1ba-8e3b12fa7724 · inbound

Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays cites this paper.

Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays Electrostatic Force Regularization for Neural Structured Pruning

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:49:40.535602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.