Paper Citation Record · LEDGER

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation

As of 14 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2507.21573.

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

pith.paper-citation-record.v1
2507.21573 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:43:06.223485Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T04:44:42.434712Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T04:45:20.008777Z

Reference resolution

45 of 45 outbound references displayed

verified exact3
verified fuzzy33
unresolved9
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 534a6ad7-9b34-4b2d-b559-74185699a314 · outbound

This paper cites DECORE: Deep compression with reinforcement learning.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation DECORE: Deep compression with reinforcement learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:15.095308Z

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Observation 63708b52-dcb1-49b4-befa-1b5da8e7e6f6 · outbound

This paper cites Automatic Neural Network Pruning that Efficiently Preserves the Model Accuracy.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Automatic Neural Network Pruning that Efficiently Preserves the Model Accuracy

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:43:06.835560Z

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Observation 97bbcc0e-3203-4678-8943-56199c6d799b · outbound

This paper cites RGP: Neural network pruning through regular graph with edges swapping.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation RGP: Neural network pruning through regular graph with edges swapping

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:14.887201Z

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Observation 406d4441-5449-4b5a-8a42-e73e2f5d13f3 · outbound

This paper cites A survey on deep neural network pruning: Taxonomy, compar- ison, analysis, and recommendations.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation A survey on deep neural network pruning: Taxonomy, compar- ison, analysis, and recommendations

Reference 4

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Observation 0bc3ce7f-96bc-4c61-bf55-fa2e35582e47 · outbound

This paper cites an unresolved cited work.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Unresolved cited work

Reference 5

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Observation 933c84f0-2490-4b2d-b30b-99b28aa6356d · outbound

This paper cites ARTHuS: Adaptive real-time human segmentation in sports through online distillation.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation ARTHuS: Adaptive real-time human segmentation in sports through online distillation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:14.312437Z

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Observation b0dc830e-3f58-4a25-b0aa-22459341c869 · outbound

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

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation ImageNet: A large-scale hierarchical image database

Reference 7

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Observation e676f77a-eb96-4ef7-8f7c-cd91f279c054 · outbound

This paper cites Network pruning via feature shift mini- mization.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Network pruning via feature shift mini- mization

Reference 8

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Observation 8ccd0dae-c8d2-45e2-a4f9-93f427df29bb · outbound

This paper cites A Differentiable Framework for End-to-End Learning of Hybrid Structured Compression.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation A Differentiable Framework for End-to-End Learning of Hybrid Structured Compression

Reference 9

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Observation 1e7e8633-1507-43ff-bd96-3d2342b227e7 · outbound

This paper cites The lottery ticket hy- pothesis: Finding sparse, trainable neural networks.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation The lottery ticket hy- pothesis: Finding sparse, trainable neural networks

Reference 10

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Observation d5db262f-6bbe-436d-9a7b-5f4d3d153ec0 · outbound

This paper cites Jointly training and pruning CNNs via learnable agent guid- ance and alignment.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Jointly training and pruning CNNs via learnable agent guid- ance and alignment

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:13.608294Z

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Observation 13ffa312-7f44-4389-8fb5-4ffd8d59c54c · outbound

This paper cites an unresolved cited work.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Unresolved cited work

Reference 12

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Observation 951ee00b-a1d0-407e-8919-99ec2f1d763d · outbound

This paper cites Automatic network pruning via Hilbert-Schmidt indepen- dence criterion lasso under information bottleneck principle.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Automatic network pruning via Hilbert-Schmidt indepen- dence criterion lasso under information bottleneck principle

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:13.309695Z

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Observation 5c6031fa-6ca0-4c29-b57b-a363e353687f · outbound

This paper cites Dynamic net- work surgery for efficient DNNs.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Dynamic net- work surgery for efficient DNNs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:13.085694Z

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Observation 5a88e1de-354d-4920-a66e-57eb1000d53c · outbound

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LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Unresolved cited work

Reference 15

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Observation acfbb924-dfab-4c90-ab24-74c3bead58f7 · outbound

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LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Unresolved cited work

Reference 16

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Observation 37680a0e-7d77-437a-b1df-86e7dca91169 · outbound

This paper cites Deep residual learning for image recognition.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Deep residual learning for image recognition

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:12.591963Z

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Observation 7b6ff315-18d6-4af4-984c-037356193e77 · outbound

This paper cites Structured pruning for deep con- volutional neural networks: A survey.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Structured pruning for deep con- volutional neural networks: A survey

Reference 18

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Observation 67de91ad-57e3-4674-a11c-08e402c928b5 · outbound

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

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Soft filter pruning for accelerating deep convolutional neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:12.242623Z

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Observation 395cd43f-2743-4fc8-8839-00511536c2a3 · outbound

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

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Filter pruning via geometric median for deep convolutional neural networks acceleration

Reference 20

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Observation c0944544-7c16-420e-9548-62b02175d347 · outbound

This paper cites Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

Reference 21

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Observation a78b5d9c-d0ac-4650-9177-6ec41930102b · outbound

This paper cites Accelerating transformer pre-training with 2:4 sparsity.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Accelerating transformer pre-training with 2:4 sparsity

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:11.886604Z

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Observation 1aed1002-ce06-4c60-af1f-7b4a3f2a59b2 · outbound

This paper cites A survey of FPGA and ASIC designs for transformer inference acceleration and optimization.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation A survey of FPGA and ASIC designs for transformer inference acceleration and optimization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:11.724292Z

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Observation 36013e8d-9b51-4435-84e7-d663e3873830 · outbound

This paper cites Learning multiple layers of features from tiny images, 2009.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Learning multiple layers of features from tiny images, 2009

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:11.578168Z

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Observation 298fd592-3576-4b6a-b806-3c537c1c6a79 · outbound

This paper cites Denker, and Sara A.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Denker, and Sara A

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:11.372867Z

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Observation 328e8d61-ca1e-4b4a-95ca-8bcbf98b75b5 · outbound

This paper cites Pruning filters for efficient ConvNets.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Pruning filters for efficient ConvNets

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:11.124972Z

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Observation a43bf957-032f-48bd-a4cd-58f4fa62df98 · outbound

This paper cites Pruning filters for efficient ConvNets.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Pruning filters for efficient ConvNets

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:10.890483Z

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Observation a093cab7-60e1-40e3-999d-87527a33f7ad · outbound

This paper cites Pruning and quantization for deep neural network acceleration: A survey.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Pruning and quantization for deep neural network acceleration: A survey

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:10.613431Z

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Observation 200023cc-cf9a-467d-8f9c-86d454890b36 · outbound

This paper cites HRank: Filter pruning using high-rank feature map.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation HRank: Filter pruning using high-rank feature map

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:10.348293Z

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Observation 713a0326-b59d-4f4a-8168-1df6bbf4e172 · outbound

This paper cites Network pruning us- ing adaptive exemplar filters.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Network pruning us- ing adaptive exemplar filters

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:10.116758Z

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Observation e1ebf25b-3580-49e7-ba1c-aaf0546a681a · outbound

This paper cites EZCrop: Energy-zoned channels for robust out- put pruning.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation EZCrop: Energy-zoned channels for robust out- put pruning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:09.822880Z

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Observation c4919687-fdda-4237-8d32-08559ae70556 · outbound

This paper cites Learning efficient convolutional networks through network slimming.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Learning efficient convolutional networks through network slimming

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:09.560355Z

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Observation a04fad71-9364-4ae3-8bb5-102e40c36ca0 · outbound

This paper cites SGDR: Stochastic gradi- ent descent with warm restarts.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation SGDR: Stochastic gradi- ent descent with warm restarts

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:09.315632Z

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Observation 22a99c2b-a2fb-48aa-ab5e-994e860994f3 · outbound

This paper cites Network pruning using linear dependency analysis on feature maps.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Network pruning using linear dependency analysis on feature maps

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:09.048009Z

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Observation e4df6289-f183-4d65-8715-0dd0d9c3cc67 · outbound

This paper cites Enhanced network compression through tensor decomposi- tions and pruning.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Enhanced network compression through tensor decomposi- tions and pruning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:08.787223Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 944057a7-ffd9-48ac-bb07-123c1519c743 · outbound

This paper cites Foundations of the theory of performance-based ranking.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Foundations of the theory of performance-based ranking

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:08.513429Z

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Observation 5e9b551b-1d95-490a-8d3f-b1b69e6a282a · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T12:43:05.681710Z

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Observation b4dd7def-29f4-4660-8803-693bd981bfe6 · outbound

This paper cites ptflops: a flops counting tool for neu- ral networks in pytorch framework.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation ptflops: a flops counting tool for neu- ral networks in pytorch framework

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:08.267820Z

Source-reported events for the cited work

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Observation f421e85c-4eb4-4dab-a076-fa8e8997ea8b · outbound

This paper cites CHIP: channel independence-based pruning for compact neural networks.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation CHIP: channel independence-based pruning for compact neural networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:08.006418Z

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Observation b99fc76a-9cde-4e3d-b033-a7d0505ff449 · outbound

This paper cites A Survey on Transformer Compression.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation A Survey on Transformer Compression

Reference 40

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Source-reported events for the cited work

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Observation add598cd-a3c3-4634-be26-aa7b9d03bf27 · outbound

This paper cites Deep learning and the information bottleneck principle.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Deep learning and the information bottleneck principle

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:07.711737Z

Source-reported events for the cited work

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Observation 20bc47f6-f531-4ed1-941a-51dff889ea06 · outbound

This paper cites Single Shot Structured Pruning Before Training.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Single Shot Structured Pruning Before Training

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T12:43:06.042118Z

Source-reported events for the cited work

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Observation deffa94e-9795-41b1-9f2f-6dc90d320fd4 · outbound

This paper cites HALOC: Hardware-aware automatic low-rank compression for com- pact neural networks.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation HALOC: Hardware-aware automatic low-rank compression for com- pact neural networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:43:07.390901Z

Source-reported events for the cited work

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Observation 5abb9d85-ad31-4c52-9fe4-c042a5f5d10b · outbound

This paper cites Toward Compact Deep Neural Networks via Energy-Aware Pruning.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Toward Compact Deep Neural Networks via Energy-Aware Pruning

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:43:06.399259Z

Source-reported events for the cited work

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Observation e01865d2-83ed-45aa-8e88-32de94e9d120 · outbound

This paper cites an unresolved cited work.

LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:43:07.107907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 6a16dcab-1580-44ec-ba75-99cdf28aa886 · inbound

Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models cites this paper.

Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-08-11T01:20:03.851321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.