Paper Citation Record · LEDGER

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks

As of 16 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:1908.02620.

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

pith.paper-citation-record.v1
1908.02620 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:00:12.076841Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

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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

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 98639418-6875-44a9-b8a2-151e11f31de8 · outbound

This paper cites Imagenet classiﬁcation with deep convolutional neural networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Imagenet classiﬁcation with deep convolutional neural networks

Reference 1

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Observation fa734715-ebc8-4277-9214-ed10613cd34a · outbound

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

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Very deep convolutional networks for large-scale image recognition

Reference 2

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Observation f3db8b41-1014-4866-8752-89f62f4947a5 · outbound

This paper cites Deep residual learning for image recognition.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Deep residual learning for image recognition

Reference 3

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Observation 2d1dc09b-cbd9-4ccd-bd57-84a5a30861bc · outbound

This paper cites Predicting parameters in deep learning.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Predicting parameters in deep learning

Reference 4

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Observation 740c2c82-a14c-4e38-95c8-b8aa178db845 · outbound

This paper cites Do deep nets really need to be deep?.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Do deep nets really need to be deep?

Reference 5

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Observation 088f854e-ebb2-4f59-8fb0-509a38179b15 · outbound

This paper cites Learning Efﬁcient Convolutional Networks through Network Slimming.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Learning Efﬁcient Convolutional Networks through Network Slimming

Reference 6

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Observation fd987334-f50c-42ef-88d2-3f14054f68ad · outbound

This paper cites Channel pruning for accelerating very deep neural networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Channel pruning for accelerating very deep neural networks

Reference 7

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Observation cdc90086-d35f-4668-8fea-b053ae32c2d5 · outbound

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

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Speeding Up Convolutional Neural Networks with Low Rank Expansions

Reference 8

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Observation 29b39da5-135d-45d1-b588-d92bf4aa0790 · outbound

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

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Exploiting linear structure within convolutional networks for efﬁcient evaluation

Reference 9

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Observation 9ac663ab-f5b5-4211-b623-6bf1f9df1253 · outbound

This paper cites Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 10

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Observation 6aa393b9-433d-4fca-b144-15608a15e9fa · outbound

This paper cites Xnor-net: Imagenet classiﬁcation using binary convolutional neural networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Xnor-net: Imagenet classiﬁcation using binary convolutional neural networks

Reference 11

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Observation 87c01640-514c-493c-b50e-da7e0a9d5ba9 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Distilling the Knowledge in a Neural Network

Reference 12

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Observation 3f487b00-c136-4ea9-bae3-3e2cc90ef8ef · outbound

This paper cites Fitnets: Hints for thin deep nets.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Fitnets: Hints for thin deep nets

Reference 13

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Observation 2d3917d7-a74d-4060-b9cc-6be3f5e8800b · outbound

This paper cites Pruning Filter via Geometric Median for Deep Convolutional Neural Net- works Acceleration.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Pruning Filter via Geometric Median for Deep Convolutional Neural Net- works Acceleration

Reference 14

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Observation 1951f6bf-5a47-40d7-a069-0f4f298a85d0 · outbound

This paper cites Optimal brain damage.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Optimal brain damage

Reference 15

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Observation 74666c8c-f883-48d1-8c23-f98ecc80c9a6 · outbound

This paper cites Second order derivatives for network pruning: Optimal brain surgeon.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Second order derivatives for network pruning: Optimal brain surgeon

Reference 16

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Observation 7fb89a6d-282c-4d6f-bf03-a45bcdc0e083 · outbound

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

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Learning both weights and connections for efﬁcient neural network

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:00:12.841287Z

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Observation 69be9c0c-1ad2-4165-9dd0-ac2366249705 · outbound

This paper cites Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:00:12.822367Z

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Observation 798b140a-01cd-4510-bf13-aa1ff55da415 · outbound

This paper cites Dynamic network surgery for efﬁcient dnns.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Dynamic network surgery for efﬁcient dnns

Reference 19

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Observation 107ff381-c8c6-4c7f-8ddd-bf605410748a · outbound

This paper cites Compressing Neural Networks using the Variational Information Bottleneck.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Compressing Neural Networks using the Variational Information Bottleneck

Reference 20

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Observation 112f2720-9c76-4cc8-9183-e01f0ff6dd32 · outbound

This paper cites Variational Dropout Sparsiﬁes Deep Neural Networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Variational Dropout Sparsiﬁes Deep Neural Networks

Reference 21

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Observation 30f83ea3-8188-4704-b747-35d00d02654d · outbound

This paper cites Data-free Parameter Pruning for Deep Neural Net- works.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Data-free Parameter Pruning for Deep Neural Net- works

Reference 22

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Observation f1acfdcd-9dd9-4608-b19d-1ffb815088fa · outbound

This paper cites Diversity networks: neural network compression using determi- nantal point processes.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Diversity networks: neural network compression using determi- nantal point processes

Reference 23

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Observation 40b7cc12-e1a1-4659-ad3f-94992de2466b · outbound

This paper cites Clustering convolutional kernels to compress deep neural networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Clustering convolutional kernels to compress deep neural networks

Reference 24

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Observation 62d2d7cb-3b79-4cf6-9abf-b1c7aaf5b8c8 · outbound

This paper cites Learning structured sparsity in deep neural networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Learning structured sparsity in deep neural networks

Reference 25

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Observation f3d6576c-15e9-4ebd-85ca-be50339afe8e · outbound

This paper cites Fast convnets using group-wise brain damage.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Fast convnets using group-wise brain damage

Reference 26

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Observation 4fd4c9c1-403f-45ad-a830-c6003a370ae6 · outbound

This paper cites Data-driven sparse structure selection for deep neural networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Data-driven sparse structure selection for deep neural networks

Reference 27

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Observation 03f7a0aa-c7d9-452c-aa9b-e98d3c3049bc · outbound

This paper cites Less is more: Towards compact cnns.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Less is more: Towards compact cnns

Reference 28

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Observation fb23aa63-e9a5-4ac3-8109-0c067064a080 · outbound

This paper cites Thinet: A ﬁlter level pruning method for deep neu- ral network compression.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Thinet: A ﬁlter level pruning method for deep neu- ral network compression

Reference 29

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Observation ff390016-3bec-41e2-8d73-eb3bc25cff5e · outbound

This paper cites Discrimination-aware channel pruning for deep neural networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Discrimination-aware channel pruning for deep neural networks

Reference 30

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Observation 3b6a8227-f3d5-452d-8b8c-65fb78d3a3bc · outbound

This paper cites Reducing duplicate ﬁlters in deep neural networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Reducing duplicate ﬁlters in deep neural networks

Reference 31

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Observation 9e5c919a-eea4-4409-9554-eba552da29b5 · outbound

This paper cites Shufﬂenet: An extremely efﬁcient convolutional neural network for mobile devices.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Shufﬂenet: An extremely efﬁcient convolutional neural network for mobile devices

Reference 32

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Observation a191a30a-271a-4b5d-a0c4-adff76a35ea7 · outbound

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

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 33

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This paper cites Designing Neural Network Architectures using Reinforcement Learning.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Designing Neural Network Architectures using Reinforcement Learning

Reference 34

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This paper cites Neural Architecture Search with Reinforcement Learning.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Neural Architecture Search with Reinforcement Learning

Reference 35

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Observation 5969a665-576f-4364-8ced-db6637f7c69b · outbound

This paper cites Large-scale evolution of image classiﬁers.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Large-scale evolution of image classiﬁers

Reference 36

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Observation fe431d6e-695c-479c-aee6-a3d5d688c78f · outbound

This paper cites Hierarchical Representations for Efﬁcient Architecture Search.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Hierarchical Representations for Efﬁcient Architecture Search

Reference 37

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Observation 6729e557-5b78-4561-b4c2-9c690cbeb508 · outbound

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Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Neural architecture optimization

Reference 38

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Observation d2dcd05f-db13-48a5-9e9e-e5d0cc1583e5 · outbound

This paper cites DARTS: Differentiable Architecture Search.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks DARTS: Differentiable Architecture Search

Reference 39

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This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 40

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Observation 093d2bd0-aa71-4a86-8d9f-c3f4101e85ff · outbound

This paper cites Deep sparse rectiﬁer neural networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Deep sparse rectiﬁer neural networks

Reference 41

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Observation 9ce9f7c5-5519-4845-90f7-9e7ca04d9f83 · outbound

This paper cites Densely connected convolutional networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Densely connected convolutional networks

Reference 42

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Observation 9bda0b96-dd65-475c-8ef0-998b8a34f2c5 · outbound

This paper cites Rethinking the value of network pruning.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Rethinking the value of network pruning

Reference 43

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Observation bb1460ad-cabb-4179-9017-6ae365867c12 · outbound

This paper cites Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:00:12.313621Z

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Observation 89c3d94d-f0c0-4a0a-9c72-0914bfcf259a · outbound

This paper cites Learning multiple layers of features from tiny images.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Learning multiple layers of features from tiny images

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:00:12.286043Z

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Observation c5c04d7d-2316-443e-8d90-4b5529553b60 · outbound

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

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Imagenet: A large-scale hierarchical image database

Reference 46

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Observation d84c045d-6eaa-46dc-ae2c-794aee29f02c · outbound

This paper cites Automatic differentiation in PyTorch.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Automatic differentiation in PyTorch

Reference 47

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Observation 1070a9dc-089e-4446-8480-5f23aea829ce · outbound

This paper cites Identity mappings in deep residual networks.

Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks Identity mappings in deep residual networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:00:12.218037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.