Paper Citation Record · LEDGER

Learning Instance-wise Sparsity for Accelerating Deep Models

As of 5 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:1907.11840.

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

pith.paper-citation-record.v1
1907.11840 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T15:15:42.863578Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact3
verified fuzzy28
unresolved1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9c8235f3-cf0f-4aaf-8d14-86a56c740697 · outbound

This paper cites Dynamic capacity networks.

Learning Instance-wise Sparsity for Accelerating Deep Models Dynamic capacity networks

Reference 1

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Observation 93c4964b-5be1-4520-a246-65dbde470e09 · outbound

This paper cites Adaptive neural networks for efﬁcient inference.

Learning Instance-wise Sparsity for Accelerating Deep Models Adaptive neural networks for efﬁcient inference

Reference 2

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Observation f8ff096c-9014-417d-8162-38e9123e68fa · outbound

This paper cites BinaryConnect: Training Deep Neural Networks with binary weights during propagations.

Learning Instance-wise Sparsity for Accelerating Deep Models BinaryConnect: Training Deep Neural Networks with binary weights during propagations

Reference 3

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Observation 06202f35-671e-4083-9012-3a24d3afaae0 · outbound

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

Learning Instance-wise Sparsity for Accelerating Deep Models Imagenet: A large-scale hierarchical image database

Reference 4

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Observation d544166f-a3ff-42c0-abb3-041c875b4a5b · outbound

This paper cites Exploiting linear structure within convolutional networks for efﬁcient evaluation.

Learning Instance-wise Sparsity for Accelerating Deep Models Exploiting linear structure within convolutional networks for efﬁcient evaluation

Reference 5

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Observation 1cd00b5b-c941-406a-814d-118e6d5b0c61 · outbound

This paper cites More is less: A more complicated network with less inference complexity.

Learning Instance-wise Sparsity for Accelerating Deep Models More is less: A more complicated network with less inference complexity

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.566348Z

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Observation 8dcc1a0b-3099-4aa6-b41d-9a2636cd155e · outbound

This paper cites Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, and Ruslan Salakhutdinov.

Learning Instance-wise Sparsity for Accelerating Deep Models Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, and Ruslan Salakhutdinov

Reference 7

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Observation 7f2a30de-a6fd-4d06-9a7a-4eb6d15008a9 · outbound

This paper cites Dynamic Channel Pruning: Feature Boosting and Suppression.

Learning Instance-wise Sparsity for Accelerating Deep Models Dynamic Channel Pruning: Feature Boosting and Suppression

Reference 8

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Observation 754aa005-d34f-40b1-b37e-0fc934425624 · outbound

This paper cites Deep compression: Compressing deep neural net- works with pruning, trained quantization and huffman cod- ing.

Learning Instance-wise Sparsity for Accelerating Deep Models Deep compression: Compressing deep neural net- works with pruning, trained quantization and huffman cod- ing

Reference 9

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Source-reported events for the cited work

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Observation 505d8486-dbb3-4315-ad7e-50f2b0e4dbab · outbound

This paper cites Deep residual learning for image recog- nition.

Learning Instance-wise Sparsity for Accelerating Deep Models Deep residual learning for image recog- nition

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.617106Z

Source-reported events for the cited work

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Observation 82b0dee0-a0e9-4ba3-9c30-d841cd8392de · outbound

This paper cites Channel Gating Neural Networks.

Learning Instance-wise Sparsity for Accelerating Deep Models Channel Gating Neural Networks

Reference 11

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Source-reported events for the cited work

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Observation faf3a786-4bed-4d97-90bf-bdc5961a5979 · outbound

This paper cites Accurate image super-resolution using very deep convolutional networks.

Learning Instance-wise Sparsity for Accelerating Deep Models Accurate image super-resolution using very deep convolutional networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.595190Z

Source-reported events for the cited work

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Observation 04b623ed-32b5-4ed7-b7b8-6d2ac7917767 · outbound

This paper cites Imagenet classiﬁcation with deep convolutional neural networks.

Learning Instance-wise Sparsity for Accelerating Deep Models Imagenet classiﬁcation with deep convolutional neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.587875Z

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Observation caca05c5-7a18-4def-9111-fed46aeceacb · outbound

This paper cites Learning multiple lay- ers of features from tiny images.

Learning Instance-wise Sparsity for Accelerating Deep Models Learning multiple lay- ers of features from tiny images

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.623436Z

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Observation e4ac94c9-5d5f-46d4-9918-d34b01fee047 · outbound

This paper cites Runtime neural pruning.

Learning Instance-wise Sparsity for Accelerating Deep Models Runtime neural pruning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.613746Z

Source-reported events for the cited work

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Observation faefa2ac-4b25-4a97-8ccb-61f9b5baed00 · outbound

This paper cites Dynamic deep neural networks: Optimizing accuracy-efﬁciency trade-offs by selective execution.

Learning Instance-wise Sparsity for Accelerating Deep Models Dynamic deep neural networks: Optimizing accuracy-efﬁciency trade-offs by selective execution

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.620210Z

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Observation 999450e1-9512-4f67-9867-2b1a8292b37d · outbound

This paper cites Learning efﬁcient convolutional networks through net- work slimming.

Learning Instance-wise Sparsity for Accelerating Deep Models Learning efﬁcient convolutional networks through net- work slimming

Reference 17

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Observation 09ef61c3-bf25-40eb-a4e9-85f38d9c0e4c · outbound

This paper cites Thinet: A ﬁlter level pruning method for deep neural network compression.

Learning Instance-wise Sparsity for Accelerating Deep Models Thinet: A ﬁlter level pruning method for deep neural network compression

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.604833Z

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Observation ec8f881e-976e-4a7c-96c6-32cc28048a43 · outbound

This paper cites Deciding how to decide: Dynamic routing in artiﬁcial neural networks.

Learning Instance-wise Sparsity for Accelerating Deep Models Deciding how to decide: Dynamic routing in artiﬁcial neural networks

Reference 19

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Observation ebf599d4-5794-41ad-abf3-96bbb9298173 · outbound

This paper cites Xnor-net: Ima- genet classiﬁcation using binary convolutional neural net- works.

Learning Instance-wise Sparsity for Accelerating Deep Models Xnor-net: Ima- genet classiﬁcation using binary convolutional neural net- works

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.606898Z

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Observation d55a089a-1dd4-4ad7-a52b-e81fc98572eb · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.

Learning Instance-wise Sparsity for Accelerating Deep Models Faster r-cnn: Towards real-time object detection with region proposal networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.599977Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 99276bab-3801-4fc9-a0ad-9073920a6f04 · outbound

This paper cites Sbnet: Sparse blocks network for fast inference.

Learning Instance-wise Sparsity for Accelerating Deep Models Sbnet: Sparse blocks network for fast inference

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4f25189c-11cf-4e95-829a-1d047cff1d1d · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Learning Instance-wise Sparsity for Accelerating Deep Models Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 23

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Observation 887aa0ea-abd6-435a-97fb-fccd18dca069 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

Learning Instance-wise Sparsity for Accelerating Deep Models Very deep convolutional networks for large-scale image recognition

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 114c9591-2cff-4b3e-bca6-e91f2bd3a691 · outbound

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Learning Instance-wise Sparsity for Accelerating Deep Models Unresolved cited work

Reference 25

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Observation 88ea4485-df8f-4807-a8bf-21dead423a98 · outbound

This paper cites Mark, Noam Shazeer, and Kayvon Fata- halian.

Learning Instance-wise Sparsity for Accelerating Deep Models Mark, Noam Shazeer, and Kayvon Fata- halian

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.596441Z

Source-reported events for the cited work

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Observation eb73b08d-e132-4191-8e1e-753041aa56c3 · outbound

This paper cites Improving the speed of neural networks on cpus.

Learning Instance-wise Sparsity for Accelerating Deep Models Improving the speed of neural networks on cpus

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.556534Z

Source-reported events for the cited work

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Observation 6375daba-7e0e-4cb0-b950-16e5ef88d925 · outbound

This paper cites Convolutional networks with adaptive inference graphs.

Learning Instance-wise Sparsity for Accelerating Deep Models Convolutional networks with adaptive inference graphs

Reference 28

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Source-reported events for the cited work

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Observation bec1eba0-c292-4117-b381-fa4ba7dc12fb · outbound

This paper cites Cnnpack: Packing convolu- tional neural networks in the frequency domain.

Learning Instance-wise Sparsity for Accelerating Deep Models Cnnpack: Packing convolu- tional neural networks in the frequency domain

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.580367Z

Source-reported events for the cited work

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Observation 950032fd-9600-4946-994a-69a911c1c075 · outbound

This paper cites Gonzalez.

Learning Instance-wise Sparsity for Accelerating Deep Models Gonzalez

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.636662Z

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Observation 62a610f6-ccbd-43ff-996b-0838cde4a44b · outbound

This paper cites Learning structured sparsity in deep neural networks.

Learning Instance-wise Sparsity for Accelerating Deep Models Learning structured sparsity in deep neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.610421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dd159fc1-f5a4-4369-beae-74bb8ae20285 · outbound

This paper cites l2, 1-norm regularized dis- criminative feature selection for unsupervised learning.

Learning Instance-wise Sparsity for Accelerating Deep Models l2, 1-norm regularized dis- criminative feature selection for unsupervised learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:16:14.613331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.