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Paper Citation Record · LEDGER

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2510.14812.

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

pith.paper-citation-record.v1
2510.14812 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:39:40.093913Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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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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  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation 7581c343-19e8-4a51-837e-4555b19dfba7 · outbound

This paper cites What is the state of neural network pruning? Proceedings of machine learning and systems, 2: 0 129--146, 2020.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks What is the state of neural network pruning? Proceedings of machine learning and systems, 2: 0 129--146, 2020

Reference 1

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Observation b19f082e-05b3-438a-88fd-dfd1f996f740 · outbound

This paper cites Structured Pruning is All You Need for Pruning CNNs at Initialization.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Structured Pruning is All You Need for Pruning CNNs at Initialization

Reference 2

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Observation 7662c570-425d-4d30-9fe0-db1841ae86a5 · outbound

This paper cites Trends in the dollar training cost of machine learning systems.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Trends in the dollar training cost of machine learning systems

Reference 3

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Observation 81a4a234-8cab-403b-bb8e-b5e7b7eae5f0 · outbound

This paper cites Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps

Reference 4

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Observation 6891cbf4-c90b-4085-af71-9ca8d77e35e6 · outbound

This paper cites Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models

Reference 5

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Observation de182e47-c5ed-4688-8e22-a7d3b6580c9a · outbound

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

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Imagenet: A large-scale hierarchical image database

Reference 6

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Observation a80e4d75-8eb8-49ff-8aa0-c0f0f7e0f546 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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Observation 66dcc101-c726-41b1-a802-1e6bfc4ba995 · outbound

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

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Rigging the lottery: Making all tickets winners

Reference 8

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Observation a43bd86a-4c64-44b3-b1d6-02f56c8dbac3 · outbound

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

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 9

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Observation 54333b8d-50bf-4568-9e5c-209d2a2d75ea · outbound

This paper cites Learning both weights and connections for efficient neural network.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Learning both weights and connections for efficient neural network

Reference 10

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Observation 1fc5419b-64c2-4594-91d5-aafccecff400 · outbound

This paper cites Accelerated sparse neural training: A provable and efficient method to find n: m transposable masks.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Accelerated sparse neural training: A provable and efficient method to find n: m transposable masks

Reference 11

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Observation 535e8174-dac2-4050-b6b6-9b9a56b7fd3c · outbound

This paper cites Training your sparse neural network better with any mask.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Training your sparse neural network better with any mask

Reference 12

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Observation 8ff90fa7-8ce1-44bb-b061-0270a957ddcb · outbound

This paper cites Exposing and exploiting fine-grained block structures for fast and accurate sparse training.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Exposing and exploiting fine-grained block structures for fast and accurate sparse training

Reference 13

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Observation 793d3538-7bc2-47a3-9ea2-345dc87693cc · outbound

This paper cites Dynamic Sparse Training with Structured Sparsity.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Dynamic Sparse Training with Structured Sparsity

Reference 14

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Observation 00a73331-c6c0-467a-8e93-d91377e5e9ce · outbound

This paper cites Towards optimal structured cnn pruning via generative adversarial learning.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Towards optimal structured cnn pruning via generative adversarial learning

Reference 15

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Observation d50a98fe-6a5a-4923-9eb2-5c83cf24d92f · outbound

This paper cites On improving deep learning generalization with adaptive sparse connectivity.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks On improving deep learning generalization with adaptive sparse connectivity

Reference 16

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Observation faa66efb-bc89-48e6-b6a9-b11e97515fa3 · outbound

This paper cites AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models

Reference 17

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Observation c5415b76-2666-4108-a0a6-098142752a07 · outbound

This paper cites Ai beats humans for the first time in physical skill game.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Ai beats humans for the first time in physical skill game

Reference 18

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Observation 4af2f12d-17ce-4d81-8410-67393bf2edc7 · outbound

This paper cites Autoshufflenet: Learning permutation matrices via an exact lipschitz continuous penalty in deep convolutional neural networks.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Autoshufflenet: Learning permutation matrices via an exact lipschitz continuous penalty in deep convolutional neural networks

Reference 19

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Observation ccc2dcee-066a-4f59-ac68-fedf327ae439 · outbound

This paper cites Building a large annotated corpus of english: The penn treebank.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Building a large annotated corpus of english: The penn treebank

Reference 20

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Observation f566814e-e5ed-4e80-89f7-28f6dcfdd237 · outbound

This paper cites Pointer Sentinel Mixture Models.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Pointer Sentinel Mixture Models

Reference 21

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Observation 7194f98f-42b2-4592-a5ed-688c3d0c8b5b · outbound

This paper cites Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Reference 22

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Observation 756fd5e7-e6a6-45d5-ad36-d94e1f9fd839 · outbound

This paper cites Variational dropout sparsifies deep neural networks.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Variational dropout sparsifies deep neural networks

Reference 23

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Observation 45465bdb-19e5-4854-9554-78a7d7ac7aa4 · outbound

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

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 24

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Observation 8359f002-58d5-4fe4-b244-7dc06f439326 · outbound

This paper cites On the number of linear regions of deep neural networks.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks On the number of linear regions of deep neural networks

Reference 25

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Observation ce16ec90-74e2-4ece-809b-a701f1d34adf · outbound

This paper cites Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization

Reference 26

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Observation bb3c69a6-7bf5-4583-8151-ae9232fef8a5 · outbound

This paper cites Cusparse library.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Cusparse library

Reference 27

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Observation 01eb8ae9-8293-4734-8947-127e935c485b · outbound

This paper cites Channel permutations for n: m sparsity.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Channel permutations for n: m sparsity

Reference 28

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Observation 9b4206da-b49d-4c64-b4b7-eac833cb42f9 · outbound

This paper cites Language models are unsupervised multitask learners.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Language models are unsupervised multitask learners

Reference 29

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Observation 7b9ec310-2273-4d5d-bd4c-78067f4cdbc1 · outbound

This paper cites Game-playing deepmind ai can beat top humans at chess, go and poker.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Game-playing deepmind ai can beat top humans at chess, go and poker

Reference 30

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Observation 1d3b11c0-68e8-412a-956e-72fb6f98b2d6 · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 31

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Observation 536cb013-267d-4d95-8d60-3bee51bde72e · outbound

This paper cites Mlp-mixer: An all-mlp architecture for vision.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Mlp-mixer: An all-mlp architecture for vision

Reference 32

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Observation 03673a12-845b-497e-8a52-82f07014e9df · outbound

This paper cites Dynamic Sparse Training of Diagonally Sparse Networks.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Dynamic Sparse Training of Diagonally Sparse Networks

Reference 33

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Observation 57ad4933-077a-4a00-bb42-e54251624848 · outbound

This paper cites Mest: Accurate and fast memory-economic sparse training framework on the edge.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Mest: Accurate and fast memory-economic sparse training framework on the edge

Reference 34

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Observation 37715f22-03cd-46a9-a830-65eeb850cf2c · outbound

This paper cites Universal structural patterns in sparse recurrent neural networks.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Universal structural patterns in sparse recurrent neural networks

Reference 35

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Observation 4a5989dd-e688-442a-b078-eedaa494c885 · outbound

This paper cites Epitopological learning and cannistraci-hebb network shape intelligence brain-inspired theory for ultra-sparse advantage in deep learning.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Epitopological learning and cannistraci-hebb network shape intelligence brain-inspired theory for ultra-sparse advantage in deep learning

Reference 36

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Observation b888641f-67f9-492f-becd-23164f72074d · outbound

This paper cites Brain-inspired sparse training enables transformers and llms to perform as fully connected.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Brain-inspired sparse training enables transformers and llms to perform as fully connected

Reference 37

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Observation 2994ebc9-076e-4fd3-b8be-709541a4fe7d · outbound

This paper cites write newline.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks write newline

Reference 38

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source=arxiv_source observed=2026-08-04T09:39:39.798511Z digest=sha256:abcf488b7f5df57307eeae325f84134d5bd77aff41b568f567a6f1fc2788ed2d

Observation f04adbf7-408f-48a3-9179-e46f347f3078 · outbound

This paper cites @esa (Ref.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks @esa (Ref

Reference 39

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source=arxiv_source observed=2026-08-04T09:39:39.896839Z digest=sha256:a227c82793a8b132ebacadad7a49f1bc69e6f635979df92cff7c10ba19f3dfab

Observation 91d62394-f3f3-48ff-a333-2843f04a716e · outbound

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SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Unresolved cited work

Reference 40

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source=arxiv_source observed=2026-08-04T09:39:40.008102Z digest=sha256:a5deefdf50cedf038a0cddc4988838febb73cd95493c1f30b0cc138c45bbf686

Observation 309a6859-dcd1-47c5-99bf-4430ad43c4f7 · outbound

This paper cites winning tickets,.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks winning tickets,

Reference 41

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