Paper Citation Record · LEDGER

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively

As of 15 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 3 inbound Pith citation observations for arXiv:2411.10507.

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

pith.paper-citation-record.v1
2411.10507 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:56:02.565020Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:44:05.184245Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T12:53:06.046088Z

Reference resolution

60 of 60 outbound references displayed

verified exact0
verified fuzzy46
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 83885557-17fb-4368-bab3-de4e719b6ca2 · outbound

This paper cites Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng

Reference 1

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This paper cites Designing neural network architectures using re- inforcement learning.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Designing neural network architectures using re- inforcement learning

Reference 2

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Observation 1532354c-31e0-41d5-a561-9b0c49134ca3 · outbound

This paper cites Shallowing deep networks: Layer- wise pruning based on feature representations.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Shallowing deep networks: Layer- wise pruning based on feature representations

Reference 3

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This paper cites Progressive dif- ferentiable architecture search: Bridging the depth gap be- tween search and evaluation.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Progressive dif- ferentiable architecture search: Bridging the depth gap be- tween search and evaluation

Reference 4

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This paper cites Rgp: Neural net- work pruning through regular graph with edges swapping.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Rgp: Neural net- work pruning through regular graph with edges swapping

Reference 5

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Observation ffeddbb6-804d-435e-bb20-798badd51b54 · outbound

This paper cites A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

Reference 6

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This paper cites Depth pruning with auxiliary networks for tinyml.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Depth pruning with auxiliary networks for tinyml

Reference 7

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This paper cites BERT: pre-training of deep bidirectional trans- formers for language understanding.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively BERT: pre-training of deep bidirectional trans- formers for language understanding

Reference 8

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This paper cites Nats-bench: Benchmarking nas algorithms for ar- chitecture topology and size.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Nats-bench: Benchmarking nas algorithms for ar- chitecture topology and size

Reference 9

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Observation 17ff99c7-3680-4168-b80d-f1a884f36873 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively An image is worth 16x16 words: Transformers for image recognition at scale

Reference 10

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This paper cites Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

Reference 11

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This paper cites One-shot layer-wise accuracy ap- proximation for layer pruning.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively One-shot layer-wise accuracy ap- proximation for layer pruning

Reference 12

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

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively The lottery ticket hypothesis: Finding sparse, trainable neural networks

Reference 13

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Observation 3bea3d32-1b77-4705-bda4-5c403599ad54 · outbound

This paper cites Network pruning via performance maximization.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Network pruning via performance maximization

Reference 14

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Observation 623647bb-d50f-4a74-b14b-41d085ef889e · outbound

This paper cites Measuring statistical dependence with hilbert- schmidt norms.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Measuring statistical dependence with hilbert- schmidt norms

Reference 15

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Observation b4724afa-3c6d-4b33-8c27-51b246eb826b · outbound

This paper cites Learn- ing both weights and connections for efficient neural net- work.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Learn- ing both weights and connections for efficient neural net- work

Reference 16

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This paper cites Eie: Effi- cient inference engine on compressed deep neural network.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Eie: Effi- cient inference engine on compressed deep neural network

Reference 17

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This paper cites Canonical correlation analysis: An overview with applica- tion to learning methods.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Canonical correlation analysis: An overview with applica- tion to learning methods

Reference 18

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This paper cites Deep residual learning for image recognition.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Deep residual learning for image recognition

Reference 19

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This paper cites Model complexity of deep learning: A survey.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Model complexity of deep learning: A survey

Reference 20

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

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Data-driven sparse struc- ture selection for deep neural networks

Reference 21

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Why tanh: choosing a sigmoidal function

Reference 22

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This paper cites Similarity of neural network represen- 9 tations revisited.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Similarity of neural network represen- 9 tations revisited

Reference 23

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Learning multiple layers of features from tiny images

Reference 24

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively DARTS+: Improved Differentiable Architecture Search with Early Stopping

Reference 25

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Hrank: Filter pruning using high-rank feature map

Reference 26

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively DARTS: differentiable architecture search

Reference 27

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This paper cites A Survey on Evolutionary Neural Architecture Search.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively A Survey on Evolutionary Neural Architecture Search

Reference 28

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Frequency-domain dynamic pruning for convolutional neu- ral networks

Reference 29

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This paper cites Under- standing the dynamics of dnns using graph modularity.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Under- standing the dynamics of dnns using graph modularity

Reference 30

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models

Reference 31

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Event-based vision meets deep learning on steering prediction for self-driving cars

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This paper cites Insights on representational similarity in neural networks with canoni- cal correlation.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Insights on representational similarity in neural networks with canoni- cal correlation

Reference 33

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This paper cites Do wide and deep networks learn the same things? uncover- ing how neural network representations vary with width and depth.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Do wide and deep networks learn the same things? uncover- ing how neural network representations vary with width and depth

Reference 34

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Yang, Zachary DeVito, Mar- tin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala

Reference 35

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Efficient neural architecture search via parameters sharing

Reference 36

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability

Reference 37

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Le, and Alexey Kurakin

Reference 38

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Unresolved cited work

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively You only look once: Unified, real-time object de- tection

Reference 40

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Bernstein, Alexander C

Reference 41

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Very deep con- volutional networks for large-scale image recognition

Reference 42

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RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Feature selection via dependence max- imization

Reference 43

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This paper cites Chip: Channel independence- based pruning for compact neural networks.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Chip: Channel independence- based pruning for compact neural networks

Reference 44

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This paper cites Mnas- net: Platform-aware neural architecture search for mobile.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Mnas- net: Platform-aware neural architecture search for mobile

Reference 45

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This paper cites Sr-init: An interpretable layer pruning method.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Sr-init: An interpretable layer pruning method

Reference 46

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Observation 6f3e681b-acb0-4767-8af1-4b4c8f73d7ed · outbound

This paper cites Manifold regularized dy- namic network pruning.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Manifold regularized dy- namic network pruning

Reference 47

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Observation 9d43fb00-725c-43f8-baf6-ebc6cbc102e3 · outbound

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

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration

Reference 48

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This paper cites Accelerate cnns from three dimensions: A comprehen- sive pruning framework.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Accelerate cnns from three dimensions: A comprehen- sive pruning framework

Reference 49

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Source-reported events for the cited work

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Observation e7f48bfb-4770-4a59-8ccb-0160176a5765 · outbound

This paper cites an unresolved cited work.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:56:02.783258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 98d1d6be-a1f8-44c9-97ac-cd9ce3f6d2e4 · outbound

This paper cites Fbnet: Hardware-aware efficient con- vnet design via differentiable neural architecture search.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Fbnet: Hardware-aware efficient con- vnet design via differentiable neural architecture search

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:02.774445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 99029fe6-f2f9-4010-8272-eadf10530176 · outbound

This paper cites an unresolved cited work.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:56:02.765398Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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This paper cites SNAS: stochastic neural architecture search.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively SNAS: stochastic neural architecture search

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:02.755753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0ad9107e-7a6e-48c8-82a7-f2944ad4f36f · outbound

This paper cites A systematic dnn weight pruning framework using alternating direction method of multipliers.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively A systematic dnn weight pruning framework using alternating direction method of multipliers

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:02.745141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2834b435-5040-42fe-a90f-1eafa5a27732 · outbound

This paper cites Practical block-wise neural network architecture gener- ation.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Practical block-wise neural network architecture gener- ation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:02.735572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 172c6861-39ed-4fe8-8463-d2ba863906b1 · outbound

This paper cites Evolutionary shallow- ing deep neural networks at block levels.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Evolutionary shallow- ing deep neural networks at block levels

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:02.725719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 81c75363-1cfe-4245-af76-5e4355be4c4f · outbound

This paper cites an unresolved cited work.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Unresolved cited work

Reference 57

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f3862137-0270-4a35-883d-3565490b79d6 · outbound

This paper cites an unresolved cited work.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Unresolved cited work

Reference 58

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 66e224db-78ab-426e-b94e-88937a6846d8 · outbound

This paper cites an unresolved cited work.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Unresolved cited work

Reference 60

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e1ee1f1f-77cf-4635-bc58-643cf8379f61 · outbound

This paper cites an unresolved cited work.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Unresolved cited work

Reference 2021

Resolution: parse uncertain; raw_fallback, observed 2026-08-12T19:56:02.912724Z

Source-reported events for the cited work

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Pith citing papers

Observation feb04c80-797e-413a-ac72-1b7e6e4b4828 · inbound

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices cites this paper.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively

Reference 13

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Observation ffef8bc4-d3a2-4864-8e95-fc7e914b18be · inbound

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers cites this paper.

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively

Reference 55

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Source-reported events for the cited work

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Observation 644b203d-df71-4c4d-ad69-bf41d1ef8ef3 · inbound

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture cites this paper.

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-05T12:53:14.870947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.