Paper Citation Record · LEDGER

Advanced deep architecture pruning using single filter performance

As of 15 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2501.12880.

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

pith.paper-citation-record.v1
2501.12880 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:46:15.756113Z

measured 54 of 54 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:39:34.353144Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T21:39:37.446283Z

Reference resolution

53 of 53 outbound references displayed

verified exact3
verified fuzzy29
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External citation measurements

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Outbound references

Observation 6ca008ae-1388-4635-89f4-40a454d8d74d · outbound

This paper cites an unresolved cited work.

Advanced deep architecture pruning using single filter performance Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T16:46:16.574651Z

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Observation 832fc47b-3502-4818-b971-e4e52416129a · outbound

This paper cites In all simulations, data augmentation derived from the original images was performed, by random horizontal flipping and translating up to four pixels in each direction.

Advanced deep architecture pruning using single filter performance In all simulations, data augmentation derived from the original images was performed, by random horizontal flipping and translating up to four pixels in each direction

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.560640Z

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Observation f760238d-dd16-46a3-a236-36c430bcf1ce · outbound

This paper cites The maximal accuracy w as determined by searching through the hyper -parameters (see below).

Advanced deep architecture pruning using single filter performance The maximal accuracy w as determined by searching through the hyper -parameters (see below)

Reference 3

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Observation 3146acdd-1697-4d11-bec7-c12fa251b255 · outbound

This paper cites The training of VGG-11 on CIFAR-100 [Fig.

Advanced deep architecture pruning using single filter performance The training of VGG-11 on CIFAR-100 [Fig

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.604744Z

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Observation f4dd1e45-e4e9-402e-8199-efee80b30cc5 · outbound

This paper cites Hyper-parameters for VGG-16 trained on CIFAR-100.

Advanced deep architecture pruning using single filter performance Hyper-parameters for VGG-16 trained on CIFAR-100

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.495486Z

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Observation ad0f294d-fb77-48e3-a951-14f6d109655c · outbound

This paper cites The learning-rate decay schedule[41] was also optimized.

Advanced deep architecture pruning using single filter performance The learning-rate decay schedule[41] was also optimized

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.518854Z

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Observation ed6eb85a-6450-4922-bc62-56ce92222a8f · outbound

This paper cites Hyper-parameters for EfficientNet-B0 trained on CIFAR-100.

Advanced deep architecture pruning using single filter performance Hyper-parameters for EfficientNet-B0 trained on CIFAR-100

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.462027Z

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Observation bfeaead9-5b56-4bc9-b604-15d3135aadbf · outbound

This paper cites Hyper-parameters for VGG-11 trained on CIFAR-100.

Advanced deep architecture pruning using single filter performance Hyper-parameters for VGG-11 trained on CIFAR-100

Reference 8

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Observation 7f0e1c69-e5b7-47dd-a1e1-0b29681190b1 · outbound

This paper cites Zhang, S.

Advanced deep architecture pruning using single filter performance Zhang, S

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.290494Z

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Observation 85b0a0e0-5bc6-4718-9525-171593b27fc6 · outbound

This paper cites The matrix was normalized by dividing it by its maximal value, resulting in each matrix having a maximum value of 1.

Advanced deep architecture pruning using single filter performance The matrix was normalized by dividing it by its maximal value, resulting in each matrix having a maximum value of 1

Reference 10

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Observation b1694015-3571-4b28-85b1-561c0d0b184f · outbound

This paper cites an unresolved cited work.

Advanced deep architecture pruning using single filter performance Unresolved cited work

Reference 11

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Observation 8745cf5d-3dac-4976-adf5-94cd83dc494d · outbound

This paper cites We used PyTorch for all the programming processes.

Advanced deep architecture pruning using single filter performance We used PyTorch for all the programming processes

Reference 12

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Observation 5fdf0b3c-8dff-443d-ac92-53670dc3c4cb · outbound

This paper cites LeCun, Y.

Advanced deep architecture pruning using single filter performance LeCun, Y

Reference 13

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Observation 183848f4-2b7e-4513-bf19-d6aeb3566087 · outbound

This paper cites Schmidhuber, Neural networks 61, 85 (2015).

Advanced deep architecture pruning using single filter performance Schmidhuber, Neural networks 61, 85 (2015)

Reference 14

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Observation ef6369af-fe18-4264-85b1-058eef8050bf · outbound

This paper cites an unresolved cited work.

Advanced deep architecture pruning using single filter performance Unresolved cited work

Reference 15

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Observation 179eb8f9-ed1e-4816-bce9-2a9eaf44c522 · outbound

This paper cites Huang, Z.

Advanced deep architecture pruning using single filter performance Huang, Z

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.363988Z

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Observation 47624faa-e971-4089-b68f-be4ed52a8ef4 · outbound

This paper cites an unresolved cited work.

Advanced deep architecture pruning using single filter performance Unresolved cited work

Reference 17

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Observation e5e9562b-515f-4b8d-acea-e51a24cf1740 · outbound

This paper cites an unresolved cited work.

Advanced deep architecture pruning using single filter performance Unresolved cited work

Reference 18

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Observation d9a1cff0-8350-49b9-b1e5-5b1069a71252 · outbound

This paper cites Tan and Q.

Advanced deep architecture pruning using single filter performance Tan and Q

Reference 19

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Observation 98e81235-ef6c-43c2-8132-f00bb6fceff8 · outbound

This paper cites Szegedy, W.

Advanced deep architecture pruning using single filter performance Szegedy, W

Reference 20

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Observation 0f0fa786-342f-4c01-9183-a38c0aa84924 · outbound

This paper cites Krizhevsky and G.

Advanced deep architecture pruning using single filter performance Krizhevsky and G

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.192570Z

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Observation 3846d086-4cdf-4338-81f7-069bb6bc8dce · outbound

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

Advanced deep architecture pruning using single filter performance MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 22

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Observation 8cf554c2-acdb-44b1-93fa-3f9900bd51ba · outbound

This paper cites Blalock, J.

Advanced deep architecture pruning using single filter performance Blalock, J

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.275812Z

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Observation 68c107f4-4058-4f5b-a2dd-f368dc955b7a · outbound

This paper cites Vadera and S.

Advanced deep architecture pruning using single filter performance Vadera and S

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.260993Z

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Observation 92505a6d-2183-4a9d-8225-8db444704a9b · outbound

This paper cites Layer Folding: Neural Network Depth Reduction using Activation Linearization.

Advanced deep architecture pruning using single filter performance Layer Folding: Neural Network Depth Reduction using Activation Linearization

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-10T16:46:15.943092Z

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Observation e3bd17e8-627a-42c4-8681-e93536080871 · outbound

This paper cites Concurrent Training and Layer Pruning of Deep Neural Networks.

Advanced deep architecture pruning using single filter performance Concurrent Training and Layer Pruning of Deep Neural Networks

Reference 26

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Observation 4e5ab9e1-b937-4c59-9963-6b131ca2d8e8 · outbound

This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.

Advanced deep architecture pruning using single filter performance The Unreasonable Ineffectiveness of the Deeper Layers

Reference 27

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Observation 1e4e15e0-2945-4500-91eb-80dda5fee0d1 · outbound

This paper cites The mechanism underlying successful deep learning.

Advanced deep architecture pruning using single filter performance The mechanism underlying successful deep learning

Reference 28

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Observation dfbaafed-f6ff-4744-a46f-a844d1a9832e · outbound

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Advanced deep architecture pruning using single filter performance Unresolved cited work

Reference 29

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Observation 505800d2-9386-4c45-b8bf-3a387e64e96a · outbound

This paper cites Janke, B.

Advanced deep architecture pruning using single filter performance Janke, B

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.232479Z

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Observation aa141380-3522-4e51-871c-4afcf17cf93b · outbound

This paper cites Kanter, Phys Rev A 37, 2739 (1988).

Advanced deep architecture pruning using single filter performance Kanter, Phys Rev A 37, 2739 (1988)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.218559Z

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Observation 46cfaabe-0de5-40a7-bc93-7d56568dfc1e · outbound

This paper cites Koonce and B.

Advanced deep architecture pruning using single filter performance Koonce and B

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.205892Z

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Observation 0a9adb1a-5269-43e4-ac6e-8d8ea28828e6 · outbound

This paper cites Clune, J.

Advanced deep architecture pruning using single filter performance Clune, J

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.058032Z

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Observation 0ef99ad4-1105-43ba-8215-a5f5b114948e · outbound

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

Advanced deep architecture pruning using single filter performance Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 34

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Observation 5856b65c-03e1-4446-90ba-cf3192f59e29 · outbound

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Advanced deep architecture pruning using single filter performance Unresolved cited work

Reference 35

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Observation 6a98e656-32c9-4dcb-9fa3-32da49761be9 · outbound

This paper cites Singh, V.

Advanced deep architecture pruning using single filter performance Singh, V

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.156576Z

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Observation b757ae0f-11a2-4565-978b-7ff2a716797f · outbound

This paper cites Tevet, R.

Advanced deep architecture pruning using single filter performance Tevet, R

Reference 37

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Observation 71f98238-81ba-4641-a741-5cf1a93bfe36 · outbound

This paper cites Weiss, T.

Advanced deep architecture pruning using single filter performance Weiss, T

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.126812Z

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Observation 21a6ba26-7593-4ab5-a8c5-24edab624f78 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Advanced deep architecture pruning using single filter performance Pruning Filters for Efficient ConvNets

Reference 39

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Observation 3764e42f-d15f-4351-9a3a-d8bf61e22f9d · outbound

This paper cites Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler.

Advanced deep architecture pruning using single filter performance Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-10T16:46:15.860688Z

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Observation 824fcb55-c44f-456b-9c2e-bd929021ca85 · outbound

This paper cites Tessier, V.

Advanced deep architecture pruning using single filter performance Tessier, V

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.105681Z

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Observation d76f1284-5dc8-435c-ae7d-31767bc0aea5 · outbound

This paper cites an unresolved cited work.

Advanced deep architecture pruning using single filter performance Unresolved cited work

Reference 42

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Observation 7470d40f-6e21-476c-a78b-fee1254db65e · outbound

This paper cites Schwartz, R.

Advanced deep architecture pruning using single filter performance Schwartz, R

Reference 43

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Observation 13a06942-a015-4c69-96e3-1b2531c8490f · outbound

This paper cites Pruned Neural Networks are Surprisingly Modular.

Advanced deep architecture pruning using single filter performance Pruned Neural Networks are Surprisingly Modular

Reference 44

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Observation 89071ef5-4983-49e2-8e99-ee35b97bfeff · outbound

This paper cites an unresolved cited work.

Advanced deep architecture pruning using single filter performance Unresolved cited work

Reference 46

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Observation 0b37d604-6085-477b-bf8b-d6c06a22d54e · outbound

This paper cites Baldi and P.

Advanced deep architecture pruning using single filter performance Baldi and P

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.018617Z

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Observation 64f1f0df-de86-4645-995c-322b0622f7ef · outbound

This paper cites Speeding up Convolutional Neural Networks with Low Rank Expansions.

Advanced deep architecture pruning using single filter performance Speeding up Convolutional Neural Networks with Low Rank Expansions

Reference 48

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Observation 6f678949-c1da-477d-8128-bd81b200fbf2 · outbound

This paper cites Vaswani, Advances in Neural Information Processing Systems (2017).

Advanced deep architecture pruning using single filter performance Vaswani, Advances in Neural Information Processing Systems (2017)

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.002097Z

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Observation 178833aa-d615-4deb-b838-7fa2560397b2 · outbound

This paper cites an unresolved cited work.

Advanced deep architecture pruning using single filter performance Unresolved cited work

Reference 50

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Observation 11138704-a941-4243-90cd-f3e8b5feaba2 · outbound

This paper cites Botev , G.

Advanced deep architecture pruning using single filter performance Botev , G

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:15.969485Z

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Observation af69ed4c-72f2-4ab6-930f-40b81c8dec53 · outbound

This paper cites L2 Regularization for Learning Kernels.

Advanced deep architecture pruning using single filter performance L2 Regularization for Learning Kernels

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T16:46:15.751837Z

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Observation d37a5d21-9e52-4db1-9738-e710f2ed1c51 · outbound

This paper cites How Does Learning Rate Decay Help Modern Neural Networks?.

Advanced deep architecture pruning using single filter performance How Does Learning Rate Decay Help Modern Neural Networks?

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T16:46:15.756113Z

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Unavailable: canonical work link unavailable.

Observation 805ea140-9cfe-4bc1-aca9-24af323ec389 · outbound

This paper cites Artificial 𝑁𝑐: denotes the number of clusters per filter expected from the A-AFCC.

Advanced deep architecture pruning using single filter performance Artificial 𝑁𝑐: denotes the number of clusters per filter expected from the A-AFCC

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:46:16.589637Z

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Pith citing papers

Observation 87a7a2ca-d635-43df-8a31-6dfaf4b29e15 · inbound

Low-latency vision transformers via large-scale multi-head attention cites this paper.

Low-latency vision transformers via large-scale multi-head attention Advanced deep architecture pruning using single filter performance

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:39:37.502933Z

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.