Paper Citation Record · LEDGER

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery

As of 14 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2411.09127.

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

pith.paper-citation-record.v1
2411.09127 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:09:53.782458Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:05:16.226076Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:05:16.321541Z

Reference resolution

39 of 39 outbound references displayed

verified exact1
verified fuzzy21
unresolved17
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3e870591-d70f-4265-8d57-eb90e2d9a58d · outbound

This paper cites Fast r-cnn,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Fast r-cnn,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.520601Z

Source-reported events for the cited work

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Observation f3995f04-9b5d-4472-8bc5-d297b1475a55 · outbound

This paper cites Learning deconvolution network for semantic segmentation,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Learning deconvolution network for semantic segmentation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.502378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2adabbc9-1df0-466e-b624-7135f6a271ea · outbound

This paper cites Mastering the game of Go without human knowledge,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Mastering the game of Go without human knowledge,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.484157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b4208296-92d5-4eb2-bdfa-64fc2320dee7 · outbound

This paper cites Deep residual learning for image recognition,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Deep residual learning for image recognition,

Reference 4

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Source-reported events for the cited work

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Observation d7a54940-d140-47f0-b735-052f4fdb2002 · outbound

This paper cites Identity mappings in deep residual networks,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Identity mappings in deep residual networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.454013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1f6ec8ec-5609-404a-bc87-79c4bbe922c3 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing inter- nal covariate shift,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Batch normalization: Accelerating deep network training by reducing inter- nal covariate shift,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.434805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d8a1def6-dbb0-4c99-9f80-e7342e8fd675 · outbound

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

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Learning both weights and connections for efficient neural networks,

Reference 7

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Source-reported events for the cited work

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Observation a6807909-a329-4ade-b0bb-f57914822081 · outbound

This paper cites What is the state of neural network pruning?,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery What is the state of neural network pruning?,

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d321751-7dcd-4683-8326-5a91aa0ee192 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Pruning Filters for Efficient ConvNets

Reference 9

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Source-reported events for the cited work

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Observation eabc906a-b195-424a-b524-fde490e69859 · outbound

This paper cites Soft filter pruning for accelerating deep convolutional neural networks,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Soft filter pruning for accelerating deep convolutional neural networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.388445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e4de8a8b-a5af-41a0-befb-a01d295e0c1f · outbound

This paper cites Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy

Reference 11

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Source-reported events for the cited work

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Observation 2605614c-8aec-4cf0-b4ff-a80d885461a8 · outbound

This paper cites SCOP: scientific control for reliable neural network pruning,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery SCOP: scientific control for reliable neural network pruning,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T21:09:53.644366Z

Source-reported events for the cited work

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Observation 32737926-1ef1-42d5-9602-ec00e5b1f078 · outbound

This paper cites Robust learning of parsimonious deep neural networks,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Robust learning of parsimonious deep neural networks,

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a02303d2-0635-4ff1-acdf-33fb68e13616 · outbound

This paper cites Shallowing deep networks: Layer-wise pruning based on feature representa- tions,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Shallowing deep networks: Layer-wise pruning based on feature representa- tions,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.346638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ebf5d842-e2b5-4a60-900d-3ee9efa2a06d · outbound

This paper cites DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T21:09:53.658530Z

Source-reported events for the cited work

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Observation c7335be9-b3c5-409b-a9bd-9357529c6310 · outbound

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

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Concurrent Training and Layer Pruning of Deep Neural Networks

Reference 16

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Source-reported events for the cited work

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Observation b2ce84bc-bb6a-4f7d-b17f-d61081d7c038 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery The lottery ticket hypothesis: Finding sparse, trainable neural networks,

Reference 17

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Source-reported events for the cited work

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Observation 0928aac6-3a70-4986-a88c-6137dde083ad · outbound

This paper cites Bayesian compression for deep learning,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Bayesian compression for deep learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.321648Z

Source-reported events for the cited work

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Observation 1de5927f-3c19-4340-96f1-ae8bcb3fa851 · outbound

This paper cites an unresolved cited work.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Unresolved cited work

Reference 19

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Source-reported events for the cited work

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Observation 3336b478-8248-417f-a33e-ceb0dcc16572 · outbound

This paper cites Variational bayesian model selection for mixture distribution,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Variational bayesian model selection for mixture distribution,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.295975Z

Source-reported events for the cited work

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Observation 71a94b8a-4cf1-4560-889a-79e819bbd0e5 · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Dropout: A simple way to prevent neural networks from overfitting,

Reference 21

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Source-reported events for the cited work

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Observation c41ae86a-924a-40ba-88ef-068ce90e23bb · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 22

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Source-reported events for the cited work

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Observation 8d948af0-1bbd-47c1-87e6-2470ce34092e · outbound

This paper cites Kushner and G.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Kushner and G

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.270724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bd6ab54b-93c3-4ae9-8915-482d7d828963 · outbound

This paper cites Layer pruning for obtaining shallower resnets,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Layer pruning for obtaining shallower resnets,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.255590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 45cbfd77-0944-4138-bf55-867a9ada5f93 · outbound

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

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Thinet: A filter level pruning method for deep neural network compression,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.239880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 00bd8669-1bb6-40a4-b7cd-2cd7ae458a3f · outbound

This paper cites Principled pruning of bayesian neural networks through variational free energy minimization,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Principled pruning of bayesian neural networks through variational free energy minimization,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.224028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation deb04457-4bf7-4909-806d-36d49853f23e · outbound

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

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Data-driven sparse structure selection for deep neural networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.208358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b6b48734-bbbd-4704-a638-0ae21ce54095 · outbound

This paper cites Layer Pruning via Fusible Residual Convolutional Block for Deep Neural Networks.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Layer Pruning via Fusible Residual Convolutional Block for Deep Neural Networks

Reference 28

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Source-reported events for the cited work

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Observation 2bc413bb-864c-49d9-aef5-4640372173b0 · outbound

This paper cites Learning efficient convolutional networks through network slimming,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Learning efficient convolutional networks through network slimming,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.080278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 59920dab-6ac6-4c49-9a03-e3975c6dc54d · outbound

This paper cites Variational dropout sparsifies deep neural networks,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Variational dropout sparsifies deep neural networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.060768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c2399bbc-8cf2-4496-92f5-5242da9bd381 · outbound

This paper cites A Scale Mixture Perspective of Multiplicative Noise in Neural Networks.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery A Scale Mixture Perspective of Multiplicative Noise in Neural Networks

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1199a4b2-307d-4aa9-93a8-9ae19d54ba3c · outbound

This paper cites Dropout as a structured shrinkage prior,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Dropout as a structured shrinkage prior,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.043764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b2db24ce-03b2-4dc3-b2b8-ef341f15675c · outbound

This paper cites Bmrs: Bayesian model reduction for structured pruning,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Bmrs: Bayesian model reduction for structured pruning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.027291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bdce9bec-85be-45e7-aa21-b1fc0a488625 · outbound

This paper cites Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 34

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Source-reported events for the cited work

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Observation 698bf67e-aa76-4ec3-becb-2123d3c55ceb · outbound

This paper cites Accelerate cnns from three dimensions: A comprehensive pruning framework,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Accelerate cnns from three dimensions: A comprehensive pruning framework,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:54.010143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7f53d3af-c568-4d69-b671-a4fcd2b34ce5 · outbound

This paper cites Towards opti- mal structured cnn pruning via generative adversarial learning,.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Towards opti- mal structured cnn pruning via generative adversarial learning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:53.992837Z

Source-reported events for the cited work

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Observation 318bd9c4-37db-4dbd-a68f-f8e078e2b221 · outbound

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

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Imagenet: A large-scale hierarchical image database,

Reference 37

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Source-reported events for the cited work

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Observation 32a655cf-f521-4daf-884c-449d803dd175 · outbound

This paper cites Cifar-10 (canadian institute for advanced research),.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Cifar-10 (canadian institute for advanced research),

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:09:53.968323Z

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Observation 951348b7-79ec-4279-bab3-ee1237b97303 · outbound

This paper cites an unresolved cited work.

Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-12T21:09:53.951735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 2f612277-8f92-4ae1-9899-32960ad99a4f · inbound

Online Training and Pruning of Deep Reinforcement Learning Networks cites this paper.

Online Training and Pruning of Deep Reinforcement Learning Networks Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:05:16.327100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.