Paper Citation Record · LEDGER

Dynamic Sparse Training of Diagonally Sparse Networks

As of 7 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2506.11449.

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

pith.paper-citation-record.v1
2506.11449 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:15:17.887587Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:39:39.275110Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

57 of 57 outbound references displayed

verified exact6
verified fuzzy29
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation acb8e67f-a596-4da7-82fd-7545c858c552 · outbound

This paper cites and Albert, R.

Dynamic Sparse Training of Diagonally Sparse Networks and Albert, R

Reference 1

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Observation 0c65c711-4611-4b73-bcad-1643c1c48c3c · outbound

This paper cites J., Frankle, J., and Guttag, J.

Dynamic Sparse Training of Diagonally Sparse Networks J., Frankle, J., and Guttag, J

Reference 2

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Observation 40e19aa5-6df1-4f8e-9572-30c231f7e5f6 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Structured Pruning is All You Need for Pruning CNNs at Initialization

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T04:15:18.722802Z

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Observation 57136f77-88a8-4ef1-80eb-f5a0bff34629 · outbound

This paper cites Sparsity Winning Twice: Better Robust Generalization from More Efficient Training.

Dynamic Sparse Training of Diagonally Sparse Networks Sparsity Winning Twice: Better Robust Generalization from More Efficient Training

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:15:18.709283Z

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Observation a03bc3e3-c953-4975-a4bf-0ebc323ccdd5 · outbound

This paper cites Which layer is learning faster? a systematic exploration of layer-wise convergence rate for deep neural networks.

Dynamic Sparse Training of Diagonally Sparse Networks Which layer is learning faster? a systematic exploration of layer-wise convergence rate for deep neural networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:19.064856Z

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Observation ed57bb53-d414-4c72-8f9a-d1a9fcf79d0a · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Trends in the dollar training cost of machine learning systems

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:19.054770Z

Source-reported events for the cited work

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Observation 729345dd-2fd7-435f-8055-f165664cb38d · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:15:18.695298Z

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Observation b08614df-628b-4ec4-80b7-315afda6cbf6 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Imagenet: A large-scale hierarchical image database

Reference 8

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Source-reported events for the cited work

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Observation 977f8781-5290-41ff-b990-5bceaf766a17 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

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Source-reported events for the cited work

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Observation 71af4c9d-60e1-47e4-8570-205ac0ffbac1 · outbound

This paper cites S., and Elsen, E.

Dynamic Sparse Training of Diagonally Sparse Networks S., and Elsen, E

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:19.038904Z

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Observation 9d4ee057-f126-4d8c-a6fc-337b5c2be220 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 11

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Source-reported events for the cited work

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Observation 7f6f46cb-5ac4-4c29-885d-22f25bc2d968 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Learning both weights and connections for efficient neural network

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:19.029862Z

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Observation d152cb25-3ae8-4cb1-8d13-4394713d80a0 · outbound

This paper cites Accelerating Transformer Pre-training with 2:4 Sparsity.

Dynamic Sparse Training of Diagonally Sparse Networks Accelerating Transformer Pre-training with 2:4 Sparsity

Reference 13

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Source-reported events for the cited work

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Observation eb264363-b1d7-4191-8313-f4a6585a3587 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Accelerated sparse neural training: A provable and efficient method to find n: m transposable masks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:19.020080Z

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Observation 2a86ee1d-d278-4d43-90fe-7ca10031e26e · outbound

This paper cites K., Ma, H., Chen, T., Ding, Y., and Wang, Z.

Dynamic Sparse Training of Diagonally Sparse Networks K., Ma, H., Chen, T., Ding, Y., and Wang, Z

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:19.009947Z

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Observation 53d379a7-a660-408d-a7a2-fc6d8e2f386a · outbound

This paper cites Top-kast: Top-k always sparse training.

Dynamic Sparse Training of Diagonally Sparse Networks Top-kast: Top-k always sparse training

Reference 16

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Observation 4518509c-4179-46b5-bb1b-c9b859935517 · outbound

This paper cites Advancing dynamic sparse training by exploring optimization opportunities.

Dynamic Sparse Training of Diagonally Sparse Networks Advancing dynamic sparse training by exploring optimization opportunities

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.991215Z

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Observation ba21eaf3-10cc-43a7-a18a-4e01981b2559 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Exposing and exploiting fine-grained block structures for fast and accurate sparse training

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.981703Z

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Observation fec6f463-af22-4611-bbde-0e662c2edf4e · outbound

This paper cites and Hinton, G.

Dynamic Sparse Training of Diagonally Sparse Networks and Hinton, G

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.972521Z

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Observation 1dba2fb0-3faa-4f49-817c-4cde8a6a2d67 · outbound

This paper cites Accurate Neural Network Pruning Requires Rethinking Sparse Optimization.

Dynamic Sparse Training of Diagonally Sparse Networks Accurate Neural Network Pruning Requires Rethinking Sparse Optimization

Reference 20

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Observation d9d85d36-5aa3-4e38-af01-d63c744a2aaa · outbound

This paper cites S., Bernaschi, M., Nutt, W., Silvestri, F., and Vella, F.

Dynamic Sparse Training of Diagonally Sparse Networks S., Bernaschi, M., Nutt, W., Silvestri, F., and Vella, F

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.963332Z

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Observation ae4f1b34-4b9d-4e5a-a0b8-3d2d4a9c22da · outbound

This paper cites Crafting papers on machine learning.

Dynamic Sparse Training of Diagonally Sparse Networks Crafting papers on machine learning

Reference 22

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Source-reported events for the cited work

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Observation 4c9d4b81-0a21-4600-a0c2-d683edb36dc6 · outbound

This paper cites Dynamic Sparse Training with Structured Sparsity.

Dynamic Sparse Training of Diagonally Sparse Networks Dynamic Sparse Training with Structured Sparsity

Reference 23

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Observation e337ac52-7dca-4964-b444-d612ec7569d4 · outbound

This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

Dynamic Sparse Training of Diagonally Sparse Networks SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 24

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Observation 692a9328-fde3-49fc-afea-0ca883c2da92 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Towards optimal structured cnn pruning via generative adversarial learning

Reference 25

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Observation dd0bc6a1-2762-4932-903e-ef7d01e03c76 · outbound

This paper cites and Wang, Z.

Dynamic Sparse Training of Diagonally Sparse Networks and Wang, Z

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.938390Z

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Observation 62e2ff50-b76c-4170-be81-a06b11055d52 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks On improving deep learning generalization with adaptive sparse connectivity

Reference 27

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Observation bd0d52fe-8eb6-4fb0-8b65-76e695a353d5 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models

Reference 28

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Observation 289693ac-41ec-4bc2-b540-6806beeba823 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Ai beats humans for the first time in physical skill game

Reference 29

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Observation 83104951-6e69-4b4e-a2c7-035a7bd45907 · outbound

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Dynamic Sparse Training of Diagonally Sparse Networks Unresolved cited work

Reference 30

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Observation f72043d6-96c0-4463-9b1d-431d8a98c81f · outbound

This paper cites Pointer Sentinel Mixture Models.

Dynamic Sparse Training of Diagonally Sparse Networks Pointer Sentinel Mixture Models

Reference 31

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Observation b3434182-04bd-466f-bd00-afc3df276a46 · outbound

This paper cites Accelerating Sparse Deep Neural Networks.

Dynamic Sparse Training of Diagonally Sparse Networks Accelerating Sparse Deep Neural Networks

Reference 32

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This paper cites C., Mocanu, E., Stone, P., Nguyen, P.

Dynamic Sparse Training of Diagonally Sparse Networks C., Mocanu, E., Stone, P., Nguyen, P

Reference 33

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Observation 5f638935-eea7-4fe0-922c-40062837fb54 · outbound

This paper cites Variational dropout sparsifies deep neural networks.

Dynamic Sparse Training of Diagonally Sparse Networks Variational dropout sparsifies deep neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.907093Z

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Observation 227376ee-6b74-4c76-b984-06bab8181380 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 35

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Source-reported events for the cited work

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Observation 90ce7414-658d-4032-85a2-07b4da0ec235 · outbound

This paper cites Importance estimation for neural network pruning.

Dynamic Sparse Training of Diagonally Sparse Networks Importance estimation for neural network pruning

Reference 36

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Observation 9cd46bb6-90ba-4ce9-aed3-c216d41b24cc · outbound

This paper cites and Wang, X.

Dynamic Sparse Training of Diagonally Sparse Networks and Wang, X

Reference 37

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Observation bfb3db17-fd5a-4647-8287-cdc359742e2c · outbound

This paper cites S., Besta, M., Vella, F., and Hoefler, T.

Dynamic Sparse Training of Diagonally Sparse Networks S., Besta, M., Vella, F., and Hoefler, T

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.875946Z

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Observation 06e341cd-7ab5-48ef-987f-b7d685c155a5 · outbound

This paper cites Language models are unsupervised multitask learners.

Dynamic Sparse Training of Diagonally Sparse Networks Language models are unsupervised multitask learners

Reference 39

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Observation 1952fe18-e200-4e61-ae24-c536886ba887 · outbound

This paper cites E., Puigcerver, J., Djolonga, J., Peyr \'e , G., and Blondel, M.

Dynamic Sparse Training of Diagonally Sparse Networks E., Puigcerver, J., Djolonga, J., Peyr \'e , G., and Blondel, M

Reference 40

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Observation 7858e193-bf33-40df-929e-318005c0add0 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Game-playing deepmind ai can beat top humans at chess, go and poker

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.850927Z

Source-reported events for the cited work

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Observation 350429b3-ba93-4a8c-8596-34381aed502b · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Dynamic Sparse Training of Diagonally Sparse Networks A Simple and Effective Pruning Approach for Large Language Models

Reference 42

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Source-reported events for the cited work

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Observation addffe9e-c86d-4ac9-b2ae-95317f84a5ef · outbound

This paper cites L., and Ganguli, S.

Dynamic Sparse Training of Diagonally Sparse Networks L., and Ganguli, S

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.839889Z

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Observation ae1a8ba8-6f04-44ea-8375-e5fb078e67d4 · outbound

This paper cites K., Joyce, K.

Dynamic Sparse Training of Diagonally Sparse Networks K., Joyce, K

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.828880Z

Source-reported events for the cited work

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Observation 91123f71-7a2b-406f-b4f6-9f44c16ce817 · outbound

This paper cites O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al.

Dynamic Sparse Training of Diagonally Sparse Networks O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.818048Z

Source-reported events for the cited work

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Observation e7fd8b2d-45e8-4de9-ad77-f721bf722307 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 46

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Source-reported events for the cited work

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Observation 10faa5a7-a836-4566-9fbb-905bd2f1f672 · outbound

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Dynamic Sparse Training of Diagonally Sparse Networks Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:15:18.806267Z

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Observation 9f07e15e-e391-4d60-ba4c-8de272312296 · outbound

This paper cites and Busato, F.

Dynamic Sparse Training of Diagonally Sparse Networks and Busato, F

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.795946Z

Source-reported events for the cited work

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Observation 3376bb61-0524-49a5-8cad-369bc5a2645a · outbound

This paper cites Pruning Before Training May Improve Generalization, Provably.

Dynamic Sparse Training of Diagonally Sparse Networks Pruning Before Training May Improve Generalization, Provably

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:15:18.418513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 58184fa3-9626-463e-9c5b-9e2a9ce7a595 · outbound

This paper cites Global vision transformer pruning with hessian-aware saliency.

Dynamic Sparse Training of Diagonally Sparse Networks Global vision transformer pruning with hessian-aware saliency

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.785703Z

Source-reported events for the cited work

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Observation a70fae9d-7254-4917-9757-0b8da493eb05 · outbound

This paper cites Width & depth pruning for vision transformers.

Dynamic Sparse Training of Diagonally Sparse Networks Width & depth pruning for vision transformers

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.776186Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e72ec91d-b684-4177-b31d-22ebdc9b0321 · outbound

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

Dynamic Sparse Training of Diagonally Sparse Networks Mest: Accurate and fast memory-economic sparse training framework on the edge

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:15:18.765956Z

Source-reported events for the cited work

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Observation 73226ca3-5aff-4b80-840d-df22a66609df · outbound

This paper cites LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning.

Dynamic Sparse Training of Diagonally Sparse Networks LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning

Reference 53

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Dynamic Sparse Training of Diagonally Sparse Networks M., Yan, G., and Li, X

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Dynamic Sparse Training of Diagonally Sparse Networks Unresolved cited work

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Dynamic Sparse Training of Diagonally Sparse Networks Unresolved cited work

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Dynamic Sparse Training of Diagonally Sparse Networks write newline

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SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks cites this paper.

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

Reference 33

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