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Paper Citation Record · LEDGER

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition

As of 13 August 2026, this Paper Citation Record lists 100 of 102 outbound references and 0 inbound Pith citation observations for arXiv:2412.11813.

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

pith.paper-citation-record.v1
2412.11813 v1

Coverage vector

measured 100 of 102 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:37:06.662946Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

100 of 102 outbound references displayed

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Outbound references

Observation d8d85645-2832-4ce9-85cd-e7de10a46528 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Imagenet classification with deep convolutional neural networks

Reference 1

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Observation 7c75723d-a270-4f60-aa12-27d2da2661d6 · outbound

This paper cites an unresolved cited work.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

Reference 2

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Observation c495165b-728c-40a1-aa9c-4b33605a9ad4 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Semi-Supervised Classification with Graph Convolutional Networks

Reference 3

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Observation 7b00c3c7-ee93-48d8-8999-26be7a6511fc · outbound

This paper cites Transductive kernel map learning and its application to image annotation.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Transductive kernel map learning and its application to image annotation

Reference 4

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Observation cfd11b10-bd0d-437b-a05e-dbf087fe370c · outbound

This paper cites Adaptive graph convolutional neural networks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Adaptive graph convolutional neural networks

Reference 5

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Observation a3efd683-05d1-4cad-8a1b-fc0f4596256e · outbound

This paper cites Directedacyclicgraphkernelsforactionrecognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Directedacyclicgraphkernelsforactionrecognition

Reference 6

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Observation 39d8f4c6-5138-491b-a996-dd88c394d42e · outbound

This paper cites A new model for learning in graph domains.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition A new model for learning in graph domains

Reference 7

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Observation 17e02242-c15c-43a6-95dd-f73762af1f36 · outbound

This paper cites Robust face recognition using dynamic space warping.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Robust face recognition using dynamic space warping

Reference 8

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Observation 4e010170-73fa-4637-88ec-ea794ba89609 · outbound

This paper cites Understanding attention and generalization in graph neural networks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Understanding attention and generalization in graph neural networks

Reference 9

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Observation 26b6c28e-ed78-40bb-8ae6-bac17c344705 · outbound

This paper cites Learning attribute representations for remote sensing ship category classification.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning attribute representations for remote sensing ship category classification

Reference 10

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Observation 6771b103-f3af-44ab-aa50-d1c5fac39e68 · outbound

This paper cites Improved knowledge distillation via teacher assistant.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Improved knowledge distillation via teacher assistant

Reference 11

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Observation 1fffc1a9-c874-43a0-b9db-807b3dac5094 · outbound

This paper cites learning-compression.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition learning-compression

Reference 12

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Observation 956137ea-1f65-4fa7-8c90-85ae8ba5eb4a · outbound

This paper cites Relevance feedback for satellite image change detection.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Relevance feedback for satellite image change detection

Reference 13

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Observation 4401171b-9cd5-45a2-bd34-6419fa1ab80e · outbound

This paper cites Morphnet: Fast & simple resource-constrained structure learning of deep networks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Morphnet: Fast & simple resource-constrained structure learning of deep networks

Reference 14

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Observation 6b4ecf9f-ad78-497c-9818-b561bb5c2db4 · outbound

This paper cites Sparse artificial neural networks using a novel smoothed lasso penalization.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sparse artificial neural networks using a novel smoothed lasso penalization

Reference 15

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Observation f497d943-8787-43d8-a92c-e9758ea504eb · outbound

This paper cites Entropy-constrained training of deep neural networks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Entropy-constrained training of deep neural networks

Reference 16

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Observation 8eb95570-ecf0-4b6b-9742-ac73d7a2917d · outbound

This paper cites Sahbi, J-Y.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi, J-Y

Reference 17

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Observation 553c5443-144f-4435-8a32-db1ea69fa32f · outbound

This paper cites Learning structured sparsity in deep neural networks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning structured sparsity in deep neural networks

Reference 18

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Observation 17099dc2-6141-4f89-806b-ea4ed3a7595c · outbound

This paper cites Learning efficient convolutional networks through network slimming.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning efficient convolutional networks through network slimming

Reference 19

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Observation b99f0a14-7288-499d-9016-7a64ac376ff7 · outbound

This paper cites Constrained optical flow for aerial image change detection.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Constrained optical flow for aerial image change detection

Reference 20

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Observation 76442ce5-981b-469f-9db6-42d891951950 · outbound

This paper cites Learning Sparse Neural Networks through $L_0$ Regularization.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning Sparse Neural Networks through $L_0$ Regularization

Reference 21

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Observation 64060b19-01c9-4118-b085-6797b9b13fcb · outbound

This paper cites Searching for mobilenetv3.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Searching for mobilenetv3

Reference 22

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Observation 1042db3c-d35f-48f5-9dcd-660661587729 · outbound

This paper cites Wang and H.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Wang and H

Reference 23

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Observation 83dec40e-78a0-4658-a4df-bb192810a9e8 · outbound

This paper cites Convolutional two-stream network fusion for video action recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Convolutional two-stream network fusion for video action recognition

Reference 24

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Observation a3db45aa-a491-45bb-85a6-8a8a596aa53e · outbound

This paper cites Transition forests: Learning discriminative temporal transitions for action recognition and detection.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Transition forests: Learning discriminative temporal transitions for action recognition and detection

Reference 25

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Observation a3958c69-30b3-473c-9c76-3478e57f4ab8 · outbound

This paper cites Bourdis, D.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Bourdis, D

Reference 26

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Observation 0630b98f-bd3d-4152-b4e2-6f8ba76b4ec2 · outbound

This paper cites First-person hand action benchmark with rgb-d videos and 3d hand pose annotations.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition First-person hand action benchmark with rgb-d videos and 3d hand pose annotations

Reference 27

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Observation 0d911d56-8a47-4f0e-b9a7-cff45d346844 · outbound

This paper cites Mazari and H.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Mazari and H

Reference 28

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Observation bbb6782f-dad5-4abc-b842-e10c3576ca76 · outbound

This paper cites Mazari and H.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Mazari and H

Reference 29

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Observation 0678519f-321a-43e8-addb-a0dab4449f5f · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 30

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Observation 8abb41ef-7847-4f11-973a-fe824a599752 · outbound

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

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Learning both weights and connections for efficient neural network

Reference 31

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Observation 4550bde1-f5b2-4859-9a0b-fa59119c64b3 · outbound

This paper cites Nonlinear cross-view sample enrichment for action recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Nonlinear cross-view sample enrichment for action recognition

Reference 32

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Observation 9f4d8b71-dfdf-41b6-9bf7-c9faee20d7f6 · outbound

This paper cites Optimal brain damage.Advances in NIPS, 2, 1989.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Optimal brain damage.Advances in NIPS, 2, 1989

Reference 33

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Observation c7c27c4a-3595-4c96-9e11-163468ea380e · outbound

This paper cites Second order derivatives for network pruning: Optimal brain surgeon.Advances in NIPS, 5, 1992.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Second order derivatives for network pruning: Optimal brain surgeon.Advances in NIPS, 5, 1992

Reference 34

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Observation 449c41a3-ce89-4b6f-99cb-3e10e205cd2f · outbound

This paper cites Coarse-to-fine deep kernel networks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Coarse-to-fine deep kernel networks

Reference 35

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Observation ba0f5344-6287-4ecd-87c7-883bff18311b · outbound

This paper cites Jointly learning heterogeneous features for rgb-d activity recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Jointly learning heterogeneous features for rgb-d activity recognition

Reference 36

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Observation 0be55033-01f5-42f1-bf1d-a4741d4cca99 · outbound

This paper cites Spatio-temporal graph convolution for skeleton based action recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Spatio-temporal graph convolution for skeleton based action recognition

Reference 37

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Observation 16fbc6cd-8975-426a-aadf-13df38508a42 · outbound

This paper cites Global co-occurrence feature learning and active coordinate system conversion for skeleton-based action recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Global co-occurrence feature learning and active coordinate system conversion for skeleton-based action recognition

Reference 38

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Observation cbb86d61-0e54-4fd5-bff1-fac8181e07c7 · outbound

This paper cites Laplacian deep kernel learning for image annotation.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Laplacian deep kernel learning for image annotation

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Observation 5237a9e4-4000-4d8a-baf4-79be9e3ee0c7 · outbound

This paper cites Graph cnns with motif and variable temporal block for skeleton-based action recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Graph cnns with motif and variable temporal block for skeleton-based action recognition

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Observation 19fe72b1-1d20-4ec4-8b3c-835496fdcbb2 · outbound

This paper cites Topologically-consistentmagnitudepruningforverylightweightgraphconvolutionalnetworks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Topologically-consistentmagnitudepruningforverylightweightgraphconvolutionalnetworks

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Observation 4ff6e3ab-f603-4eba-9aa9-de890698850c · outbound

This paper cites Spatial temporal graph convolutional networks for skeleton-based action recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Spatial temporal graph convolutional networks for skeleton-based action recognition

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source=pdf_text observed=2026-08-11T14:37:06.423664Z digest=sha256:cc086d5e34979efa53dce8e9663c6cc39371579b999d684860753c0b46f4404f

Observation eb819e97-93f0-4cf7-9490-4deffe09e422 · outbound

This paper cites A riemannian network for spd matrix learning.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition A riemannian network for spd matrix learning

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source=pdf_text observed=2026-08-11T14:37:06.427667Z digest=sha256:40871113fb8a448af07639f9a956db0fac8d74beee1c8bdf151486b5b0fe434f

Observation 7e9389aa-d826-40b4-bb26-56c784551ac5 · outbound

This paper cites Sahbi and F.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi and F

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source=pdf_text observed=2026-08-11T14:37:06.431785Z digest=sha256:abd2e27f0e3a4a7be948ed3d716b0d8513d1043f24b24d439029eaeca52dcb19

Observation bfc62371-82f3-42a7-a6ec-b97eee10b46b · outbound

This paper cites Jiu and H.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Jiu and H

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source=pdf_text observed=2026-08-11T14:37:06.436569Z digest=sha256:1a49e80266e5f898893ed647bca64cf3deadff8cc3e1800c1b81d1aebbfee1a6

Observation 2782a811-2940-4bf7-9a30-32922f5908fc · outbound

This paper cites A novel geometric framework on gram matrix trajectories for human behavior understanding.IEEE TPAMI, 42(1):1–14, 2018.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition A novel geometric framework on gram matrix trajectories for human behavior understanding.IEEE TPAMI, 42(1):1–14, 2018

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source=pdf_text observed=2026-08-11T14:37:06.441098Z digest=sha256:c28b93e23a1e41d9ddbf625fba7a638044f2be87c6192dd273b7d6c346e96b14

Observation d414c0db-ad75-471c-9c6d-e9967c5451b7 · outbound

This paper cites Camera pose estimation using visual servoing for aerial video change detection.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Camera pose estimation using visual servoing for aerial video change detection

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source=pdf_text observed=2026-08-11T14:37:06.445434Z digest=sha256:09e6177c31cbce3385b2a3a61476f8cbc6927e018e2b8190e214cb38d6fb8563

Observation f1f436ea-bda2-4bbe-8f5c-f4b672fc01b2 · outbound

This paper cites an unresolved cited work.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

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source=pdf_text observed=2026-08-11T14:37:06.449654Z digest=sha256:350091347eb94c14c333be6636f2f090af02f5c3f9494fe127bc80486cd915dc

Observation 88a1f333-995f-4714-8796-40c56be4d8aa · outbound

This paper cites HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition

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source=pdf_text observed=2026-08-11T14:37:06.453696Z digest=sha256:7c3d2f4b8afc52ce11955f471f61258753672d4887fa562800205daae6c75586

Observation 35d2eaa6-320a-484b-be28-21cbdb11f42e · outbound

This paper cites Building deep networks on grassmann manifolds.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Building deep networks on grassmann manifolds

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source=pdf_text observed=2026-08-11T14:37:06.457840Z digest=sha256:3ba514bda83095d3697b3907d2d0a0a2e0c14f46a17dcb24d2531e985e95aa02

Observation fb4cfa23-4410-45f1-8e3d-9f07dc81c839 · outbound

This paper cites Decoupled representation learning for skeleton-based gesture recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Decoupled representation learning for skeleton-based gesture recognition

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source=pdf_text observed=2026-08-11T14:37:06.461804Z digest=sha256:e4efb769bec0eaf0e5f6403476e05ac2a10d576b1bfbecdcfc0da291f35f8119

Observation ed013a48-44bc-4938-a585-2c3c46e691ed · outbound

This paper cites an unresolved cited work.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

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source=pdf_text observed=2026-08-11T14:37:06.465507Z digest=sha256:b98e24defff77340901e38cad3070c96f668b56cffa364254ecd5c6a42f6e3f2

Observation 926a7450-7356-4cbf-bec8-6ebbb0fc3640 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Adam: A Method for Stochastic Optimization

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source=pdf_text observed=2026-08-11T14:37:06.469423Z digest=sha256:dc081d300f40bae6ff2bd3e07450a06a8919f275d3138711af67760d22a54c34

Observation 2e73a2f9-e992-4f7e-9bb2-056d096e117a · outbound

This paper cites Structured pruning of neural networks with budget-aware regularization.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Structured pruning of neural networks with budget-aware regularization

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source=pdf_text observed=2026-08-11T14:37:06.474297Z digest=sha256:dd3846c1813a4c43a3c76fd073bea4f87c5b2197f2da17f2e08fc712d87f0f73

Observation 0e4d1562-e665-4ac9-acb5-160cf29c1389 · outbound

This paper cites Bags-of-daglets for action recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Bags-of-daglets for action recognition

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source=pdf_text observed=2026-08-11T14:37:06.478169Z digest=sha256:b3c26d1bf4ea817961c02dd8640bf1dbc8d1edfc06df452499082fd34f0fa060

Observation 43aad6ba-00d9-4d2f-b737-78c6ec2d6d6c · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Pruning Filters for Efficient ConvNets

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source=pdf_text observed=2026-08-11T14:37:06.482566Z digest=sha256:c20cfd0c156a14486193cacde4a9dc54ca3a6e4678671dd4b0c0a10b6daf5d83

Observation 746e6782-f97c-40a3-8ce7-947f97ea2d01 · outbound

This paper cites an unresolved cited work.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

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source=pdf_text observed=2026-08-11T14:37:06.486543Z digest=sha256:956668200383ab323a94ffa182f3c34a8f9f30f5a51192b0c5afe439cc7d1445

Observation b6844c80-dd62-47bc-9e41-41c6519c3318 · outbound

This paper cites Hierarchical recurrent neural network for skeleton based action recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Hierarchical recurrent neural network for skeleton based action recognition

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source=pdf_text observed=2026-08-11T14:37:06.490578Z digest=sha256:503081b397f5803ffff4fdaf3ae7b044334c0d814af74e1dda4bcae8d9b17900

Observation 7e2104d6-deee-4066-9b11-b4c6fa1d72bd · outbound

This paper cites Spatio-temporal lstm with trust gates for 3d human action recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Spatio-temporal lstm with trust gates for 3d human action recognition

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source=pdf_text observed=2026-08-11T14:37:06.494362Z digest=sha256:ad53852d5d3049c738b3ebdb6103e878d28b621bcd53f0f6959314ca08b896fb

Observation 46314df8-0739-4a66-a5ba-d24b898cba74 · outbound

This paper cites Interactive satellite image change detection with context-aware canonical correlation analysis.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Interactive satellite image change detection with context-aware canonical correlation analysis

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Observation ecb11693-1414-42fd-bd37-4fe11e6f7035 · outbound

This paper cites Skeleton-based human action recognition with global context-aware attention lstm networks.IEEE Transactions on Image Processing, 27(4):1586–1599, 2017.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Skeleton-based human action recognition with global context-aware attention lstm networks.IEEE Transactions on Image Processing, 27(4):1586–1599, 2017

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Observation 53e1229c-2d57-468d-a17e-1fee914ef7dc · outbound

This paper cites View adaptive recurrent neural networks for high performance human action recognition from skeleton data.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition View adaptive recurrent neural networks for high performance human action recognition from skeleton data

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Observation 0292bb51-8833-4eb8-8360-a101abb2471f · outbound

This paper cites an unresolved cited work.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

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source=pdf_text observed=2026-08-11T14:37:06.509926Z digest=sha256:07c28a77a39c0b235d9d2cb7160f01bc5e583ae997696872c76acc107b32326f

Observation 990e7f00-a38d-4505-8bdc-88640956cba1 · outbound

This paper cites Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks

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source=pdf_text observed=2026-08-11T14:37:06.513167Z digest=sha256:7e46b5ac25a39981b268db9829aeba23ca68557b79c9420478f68767348a2e56

Observation 3ee4c11a-ec8c-477c-8b77-7c19012f21cf · outbound

This paper cites Deepgru: Deep gesture recognition utility.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Deepgru: Deep gesture recognition utility

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source=pdf_text observed=2026-08-11T14:37:06.516605Z digest=sha256:5f64b87f1acd6d5d08f9b793897e1499a4c05127deffd78e1d25ff583a8dc6f9

Observation b20e8d82-d1e8-4d32-97a5-5a77c7020038 · outbound

This paper cites Rgb-d-based human motion recognition with deep learning: A survey.CVIU, 2018.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Rgb-d-based human motion recognition with deep learning: A survey.CVIU, 2018

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source=pdf_text observed=2026-08-11T14:37:06.520435Z digest=sha256:985cfa57845325881b2edbe5411a298bc6160e4d193c329764d6b03084db9951

Observation 6efac2a8-0cf0-451f-94ea-c123a4529d64 · outbound

This paper cites an unresolved cited work.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

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source=pdf_text observed=2026-08-11T14:37:06.525717Z digest=sha256:97ec939daafa6ed3eea90e170875a0eab4dac1b338d7966934469a6fb105b888

Observation d80aebd3-3238-446b-849a-74104262f9d5 · outbound

This paper cites Linear-time online action detection from 3d skeletal data using bags of gesturelets.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Linear-time online action detection from 3d skeletal data using bags of gesturelets

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-11T14:37:06.529436Z digest=sha256:93b04c9dad86bb61d4dafa69e535b182ca997ce611e7408f1231ec6e2b574165

Observation b0b23549-5289-4cc8-bd21-63aaaae0a4bb · outbound

This paper cites an unresolved cited work.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

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source=pdf_text observed=2026-08-11T14:37:06.533384Z digest=sha256:293fc427a9f562a55a6e846d29968644f277f8e5ceb689f38298cf00f144efcd

Observation be0b8c60-1acb-462d-a581-499e7d9df2b4 · outbound

This paper cites Applying interest operators in semi-fragile video watermarking.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Applying interest operators in semi-fragile video watermarking

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source=pdf_text observed=2026-08-11T14:37:06.537640Z digest=sha256:7f0af625db6b148cddd95eff9d665dc3591780b62d0b6a08cd644aa7ed2c70de

Observation a13876b1-7511-4025-8ab5-882537f8cf4a · outbound

This paper cites DropNeuron: Simplifying the Structure of Deep Neural Networks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition DropNeuron: Simplifying the Structure of Deep Neural Networks

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Observation 7557a83e-2d7f-4ae2-b5e6-ecb5e5289340 · outbound

This paper cites Using entropy for image and video authentication watermarks.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Using entropy for image and video authentication watermarks

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source=pdf_text observed=2026-08-11T14:37:06.545621Z digest=sha256:ce1edc3442fdc9a2d8ecbb6e0862dcd42513e76845283f771b7b6fae14245c67

Observation d971f031-b1be-4299-8098-c8f6b26a9bc7 · outbound

This paper cites An end-to-end spatio-temporal attention model for human action recognition from skeleton data.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition An end-to-end spatio-temporal attention model for human action recognition from skeleton data

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source=pdf_text observed=2026-08-11T14:37:06.549248Z digest=sha256:0f38eb31c8e1efc0a4bf3c85bc7503cff31168019262698eceae2607db379ec2

Observation 08078365-baa3-49c8-9460-f74d6c1f83ac · outbound

This paper cites Human action recognition by representing 3d skeletons as points in a lie group.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Human action recognition by representing 3d skeletons as points in a lie group

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source=pdf_text observed=2026-08-11T14:37:06.553059Z digest=sha256:868e1439a269cc2f0c195514f13c84380c43e24cb5b0fe5f400c8f3dda557178

Observation 1b6a9333-dc46-4200-9651-dae1d10ee9f9 · outbound

This paper cites From coarse to fine skin and face detection.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition From coarse to fine skin and face detection

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source=pdf_text observed=2026-08-11T14:37:06.556839Z digest=sha256:e332922fc28f559df7b20d22a80ce3d2650d8dc5ebb546159d70b0b548d20fd9

Observation f7011570-98fc-4344-8c71-ecdc69eed704 · outbound

This paper cites Regularization of neural networks using dropconnect.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Regularization of neural networks using dropconnect

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source=pdf_text observed=2026-08-11T14:37:06.560625Z digest=sha256:399f9e34b247fbee2e2cd4eae1c77ff0408436020ea59de581f133ad1fabd039

Observation 867e3853-fb9f-4eab-99ea-ca39eec37632 · outbound

This paper cites Effective 3d action recognition using eigenjoints.Journal of Visual Communication and Image Representation, 25(1):2–11, 2014.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Effective 3d action recognition using eigenjoints.Journal of Visual Communication and Image Representation, 25(1):2–11, 2014

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Observation 9727cc84-94f5-4598-bc55-aefc1f92a6cd · outbound

This paper cites Yuan, G-S.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Yuan, G-S

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Observation c40aca60-14ad-4f1d-b3b1-b4fc49feeca2 · outbound

This paper cites Interactive body part contrast mining for human interaction recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Interactive body part contrast mining for human interaction recognition

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Observation bebc47af-13a3-461d-820c-9f2e42ad973d · outbound

This paper cites Category-blind human action recognition: A practical recognition system.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Category-blind human action recognition: A practical recognition system

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source=pdf_text observed=2026-08-11T14:37:06.578237Z digest=sha256:0094550a05e74687ceb1c655f55eb0b1fb692085a11538f971b7c35efaf3d27d

Observation 58d15933-2c0d-427d-8410-90c1ff8f0eea · outbound

This paper cites Sahbi and F.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi and F

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Observation 5b2a72fe-33c2-402e-8573-917e4dfa415d · outbound

This paper cites Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences

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Observation c8b79640-47d3-4158-a5e3-212022888291 · outbound

This paper cites 3d action recognition from novel viewpoints.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition 3d action recognition from novel viewpoints

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source=pdf_text observed=2026-08-11T14:37:06.593911Z digest=sha256:7137eefaf6907eea336bdc95dc9ab860e70b64c4b1c42d9496efa4fad712058e

Observation 52f3f9ca-31ba-44de-b6e0-e46b5be899b3 · outbound

This paper cites Sahbi and D.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Sahbi and D

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Observation add2340b-b81c-461a-b5a9-d0e7e488970d · outbound

This paper cites Two-person interaction detection using body-pose features and multiple instance learning.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Two-person interaction detection using body-pose features and multiple instance learning

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source=pdf_text observed=2026-08-11T14:37:06.602088Z digest=sha256:b36b34c3043ca3fdebfb9cb9ff4767368c58cbfeda1c4b55c7a7436c4e220c36

Observation 47bbb15f-5648-4d34-a29b-d030ac99ecf5 · outbound

This paper cites Jiu and H.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Jiu and H

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source=pdf_text observed=2026-08-11T14:37:06.606129Z digest=sha256:bc62851984d021bf0923a1a04056647f1569f135edc9c9a07faa7970e654e1f0

Observation 5a2bc9f5-1dff-408a-85e2-773bb00c4534 · outbound

This paper cites The moving pose: An efficient 3d kinematics descriptor for low-latency action recognition and detection.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition The moving pose: An efficient 3d kinematics descriptor for low-latency action recognition and detection

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source=pdf_text observed=2026-08-11T14:37:06.609790Z digest=sha256:a12301b946cca9df89e76f4399b481f7514555c057432a1f4cf914d1aca8ccf0

Observation 245626fd-e9bc-4b04-95df-cc838309b63f · outbound

This paper cites Graph-cut transducers for relevance feedback in content based image retrieval.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Graph-cut transducers for relevance feedback in content based image retrieval

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source=pdf_text observed=2026-08-11T14:37:06.613789Z digest=sha256:87c8aaa65754f1e7c2ec151897538d3310b3ed52eeb61b0b3c89f1bcc08859a8

Observation c65eb864-e574-40c7-beb2-5fdd798228ef · outbound

This paper cites Efficient temporal sequence comparison and classification using gram matrix embeddings on a riemannian manifold.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Efficient temporal sequence comparison and classification using gram matrix embeddings on a riemannian manifold

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source=pdf_text observed=2026-08-11T14:37:06.617145Z digest=sha256:db3429b6007d624333665ebe79adbd85c3c15653305a2712de935583356ace05

Observation 0ab41ca0-9025-44e4-8c7a-0fc8b39ed181 · outbound

This paper cites Context-dependent kernel design for object matching and recognition.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Context-dependent kernel design for object matching and recognition

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Observation e4b4b66c-3b3c-480f-8e7f-5511fabbf8d6 · outbound

This paper cites Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition.Pattern Recognition, 76:80–94, 2018.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition.Pattern Recognition, 76:80–94, 2018

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source=pdf_text observed=2026-08-11T14:37:06.624175Z digest=sha256:792ef4597cf4261e78ad6c0cb4d321c94c54203ad8b6fb48d0438c8ae69efc91

Observation a35a2dba-88bf-4d4c-9108-6cd63f24ed81 · outbound

This paper cites Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization

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source=pdf_text observed=2026-08-11T14:37:06.628669Z digest=sha256:9a6b7c43a3e96760db5b4a41fa131babf5443ea764b843180bef1576023107ba

Observation 6c5bcc95-bfe1-4b51-ae81-e4f579c61c9c · outbound

This paper cites an unresolved cited work.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Unresolved cited work

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Observation b58ae64f-1ddc-4cff-b9a8-64d9de3097bd · outbound

This paper cites TELECOM ParisTech at ImageClefphoto 2008: Bi-Modal Text and Image Retrieval with Diversity Enhancement.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition TELECOM ParisTech at ImageClefphoto 2008: Bi-Modal Text and Image Retrieval with Diversity Enhancement

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source=pdf_text observed=2026-08-11T14:37:06.637628Z digest=sha256:cb046952697397024b7b5ccdc63f6448146b366b6fed71734a270f7c791931ba

Observation 6be4e555-0d70-4a9a-a128-a73fb1264e13 · outbound

This paper cites CNRS-TELECOM ParisTech at ImageCLEF 2013 Scalable Concept Image Annotation Task: Winning Annotations with Context Dependent SVMs.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition CNRS-TELECOM ParisTech at ImageCLEF 2013 Scalable Concept Image Annotation Task: Winning Annotations with Context Dependent SVMs

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source=pdf_text observed=2026-08-11T14:37:06.641589Z digest=sha256:fa0739c1d322cb45707275df6c86f3ce25c9b1ed7d1e5037df9e6afb18442dae

Observation d1bebb97-88d3-469b-abbf-67d9464218f0 · outbound

This paper cites Coarse-to-fine support vector classifiers for face detection.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Coarse-to-fine support vector classifiers for face detection

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source=pdf_text observed=2026-08-11T14:37:06.645656Z digest=sha256:aa8cca41be94e1c7a1dd237a74beb7c9bbb4fef3754af6c1c85cd7331c0cbc30

Observation 6608aef1-46f9-4f4e-9c17-d0795ccf7025 · outbound

This paper cites Visual content extraction for automatic semantic annotation of video news.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Visual content extraction for automatic semantic annotation of video news

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source=pdf_text observed=2026-08-11T14:37:06.650388Z digest=sha256:b417b79f49f51addfbc5a01d031c487845d25579f481f82b856b44b3d5181565

Observation 47b089b6-41f7-4edb-97f1-8a5d531cb1cd · outbound

This paper cites Misalignment resilient cca for interactive satellite image change detection.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Misalignment resilient cca for interactive satellite image change detection

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source=pdf_text observed=2026-08-11T14:37:06.654360Z digest=sha256:94f1fb8db3f59fbfbd584eae96fc318f2e9547983855d2ab9b65b8bb39c87f1c

Observation b9f31299-aa65-48e7-abd6-7186e4b90ae3 · outbound

This paper cites From 2D silhouettes to 3D object retrieval: contributions and benchmarking.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition From 2D silhouettes to 3D object retrieval: contributions and benchmarking

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source=pdf_text observed=2026-08-11T14:37:06.658222Z digest=sha256:2261c54678e4a806349578e67b96709bbc3595c4fcc4fa74bb3cdb22dbe1031e

Observation 735a17bc-1308-4252-92e1-49aed03fcb79 · outbound

This paper cites Semi supervised deep kernel design for image annotation.

Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition Semi supervised deep kernel design for image annotation

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Pith citing papers

No inbound Pith citation observations are available.