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Paper Citation Record · LEDGER

A Tour of Convolutional Networks Guided by Linear Interpreters

As of 23 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:1908.05168.

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

pith.paper-citation-record.v1
1908.05168 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:25:43.708095Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

51 of 51 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b222c0e7-a837-4e23-b6a5-370bf4215b15 · outbound

This paper cites Sanity checks for saliency maps.

A Tour of Convolutional Networks Guided by Linear Interpreters Sanity checks for saliency maps

Reference 1

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Observation 2075b20f-7134-4c56-af43-586d9b0970ae · outbound

This paper cites On pixel-wise explanations for non-linear classi- fier decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015.

A Tour of Convolutional Networks Guided by Linear Interpreters On pixel-wise explanations for non-linear classi- fier decisions by layer-wise relevance propagation.PloS one, 10(7):e0130140, 2015

Reference 2

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This paper cites A Forward-Backward Approach for Visualizing Information Flow in Deep Networks.

A Tour of Convolutional Networks Guided by Linear Interpreters A Forward-Backward Approach for Visualizing Information Flow in Deep Networks

Reference 3

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Observation 23ea05e8-b9f4-42fc-bcfa-e1d301fc700e · outbound

This paper cites Network dissection: Quantifying inter- pretability of deep visual representations.

A Tour of Convolutional Networks Guided by Linear Interpreters Network dissection: Quantifying inter- pretability of deep visual representations

Reference 4

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Observation b086df93-dadd-4a91-a014-26c21098a07f · outbound

This paper cites Tenenbaum, William T.

A Tour of Convolutional Networks Guided by Linear Interpreters Tenenbaum, William T

Reference 5

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Observation eda64251-6032-406e-93a1-fc20f1d46e32 · outbound

This paper cites Layer-wise relevance propagation for neural networks with local renormalization layers.

A Tour of Convolutional Networks Guided by Linear Interpreters Layer-wise relevance propagation for neural networks with local renormalization layers

Reference 6

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Observation 6f052037-9e56-4015-987e-760794237fb9 · outbound

This paper cites Foun- dations of data science.Vorabversion eines Lehrbuchs, 2016.

A Tour of Convolutional Networks Guided by Linear Interpreters Foun- dations of data science.Vorabversion eines Lehrbuchs, 2016

Reference 7

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Observation a940fdb7-642d-4f4b-ba47-0137678ac95b · outbound

This paper cites Template matching techniques in computer vision: theory and practice.

A Tour of Convolutional Networks Guided by Linear Interpreters Template matching techniques in computer vision: theory and practice

Reference 8

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Observation fb4d4b22-d1b2-498a-90f4-ebcc3cc305d2 · outbound

This paper cites Numerical analysis.

A Tour of Convolutional Networks Guided by Linear Interpreters Numerical analysis

Reference 9

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This paper cites Diverse feature visualizations reveal invariances in early layers of deep neural networks.

A Tour of Convolutional Networks Guided by Linear Interpreters Diverse feature visualizations reveal invariances in early layers of deep neural networks

Reference 10

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Observation c648eb73-706c-44d6-9531-684ca8ea1808 · outbound

This paper cites Understanding Deep Architectures by Visual Summaries.

A Tour of Convolutional Networks Guided by Linear Interpreters Understanding Deep Architectures by Visual Summaries

Reference 11

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Observation 806c8597-c441-4637-80aa-9c04764d6d8e · outbound

This paper cites Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives.

A Tour of Convolutional Networks Guided by Linear Interpreters Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives

Reference 12

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This paper cites TIP: Typifying the Interpretability of Procedures.

A Tour of Convolutional Networks Guided by Linear Interpreters TIP: Typifying the Interpretability of Procedures

Reference 13

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Observation 5d049e28-d053-4bc4-9c3b-7b4246e2559a · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

A Tour of Convolutional Networks Guided by Linear Interpreters Towards A Rigorous Science of Interpretable Machine Learning

Reference 14

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Observation cecd741f-0d70-424d-bbe6-af9f374e7a46 · outbound

This paper cites Towards Explanation of DNN-based Prediction with Guided Feature Inversion.

A Tour of Convolutional Networks Guided by Linear Interpreters Towards Explanation of DNN-based Prediction with Guided Feature Inversion

Reference 15

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Observation de837375-6bc1-4dd6-bc1d-353d7b15b89c · outbound

This paper cites Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks.

A Tour of Convolutional Networks Guided by Linear Interpreters Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks

Reference 16

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This paper cites ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.

A Tour of Convolutional Networks Guided by Linear Interpreters ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 17

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This paper cites Interpretation of Neural Networks is Fragile.

A Tour of Convolutional Networks Guided by Linear Interpreters Interpretation of Neural Networks is Fragile

Reference 18

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This paper cites Explaining and harnessing adversarial examples.

A Tour of Convolutional Networks Guided by Linear Interpreters Explaining and harnessing adversarial examples

Reference 19

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This paper cites PatchShuffle Regularization.

A Tour of Convolutional Networks Guided by Linear Interpreters PatchShuffle Regularization

Reference 20

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This paper cites On the Decision Boundary of Deep Neural Networks.

A Tour of Convolutional Networks Guided by Linear Interpreters On the Decision Boundary of Deep Neural Networks

Reference 21

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A Tour of Convolutional Networks Guided by Linear Interpreters Enhanced deep residual networks for single image super–resolution

Reference 22

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A Tour of Convolutional Networks Guided by Linear Interpreters Unresolved cited work

Reference 23

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A Tour of Convolutional Networks Guided by Linear Interpreters A Wavelet Tour of Signal Processing

Reference 24

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This paper cites Ex- plaining nonlinear classification decisions with deep taylor decomposition.

A Tour of Convolutional Networks Guided by Linear Interpreters Ex- plaining nonlinear classification decisions with deep taylor decomposition

Reference 25

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This paper cites On the number of linear regions of deep neural networks.

A Tour of Convolutional Networks Guided by Linear Interpreters On the number of linear regions of deep neural networks

Reference 26

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A Tour of Convolutional Networks Guided by Linear Interpreters CNN fixations: An unraveling approach to visualize the dis- criminative image regions

Reference 27

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A Tour of Convolutional Networks Guided by Linear Interpreters Multigrid Backprojection Super-Resolution and Deep Filter Visualization

Reference 28

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A Tour of Convolutional Networks Guided by Linear Interpreters Feature visualization

Reference 29

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A Tour of Convolutional Networks Guided by Linear Interpreters The building blocks of interpretability

Reference 30

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A Tour of Convolutional Networks Guided by Linear Interpreters Emergence of simple–cell receptive field properties by learning a sparse code for natural images

Reference 31

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This paper cites The Matrix Calculus You Need For Deep Learning.

A Tour of Convolutional Networks Guided by Linear Interpreters The Matrix Calculus You Need For Deep Learning

Reference 32

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A Tour of Convolutional Networks Guided by Linear Interpreters Proakis and Dimitris K

Reference 33

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This paper cites How convolutional neural network see the world - A survey of convolutional neural network visualization methods.

A Tour of Convolutional Networks Guided by Linear Interpreters How convolutional neural network see the world - A survey of convolutional neural network visualization methods

Reference 34

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Observation 1340e7bb-6018-4b10-9e4a-1573cea4d0fb · outbound

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A Tour of Convolutional Networks Guided by Linear Interpreters Ribeiro, Sameer Singh, and Carlos Guestrin

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 4f8e1450-3d4c-4e79-9853-b3cec326776a · outbound

This paper cites The perceptron: a probabilistic model for information storage and organization in the brain.

A Tour of Convolutional Networks Guided by Linear Interpreters The perceptron: a probabilistic model for information storage and organization in the brain

Reference 36

Resolution
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Observation 51345c91-6009-4be3-9581-1950e88aa191 · outbound

This paper cites Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities.

A Tour of Convolutional Networks Guided by Linear Interpreters Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities

Reference 37

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Observation 78f4a671-bb22-4385-937b-088338364e4e · outbound

This paper cites Berg, and Li Fei-Fei.

A Tour of Convolutional Networks Guided by Linear Interpreters Berg, and Li Fei-Fei

Reference 38

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 984e6931-4400-461a-8135-2760cd529a71 · outbound

This paper cites Numerical methods for large eigenvalue prob- lems: revised edition, volume 66.

A Tour of Convolutional Networks Guided by Linear Interpreters Numerical methods for large eigenvalue prob- lems: revised edition, volume 66

Reference 39

Resolution
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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 0faa61dd-6acf-4ef5-b82c-adbe33318982 · outbound

This paper cites Learning important features through propagating activation differences.

A Tour of Convolutional Networks Guided by Linear Interpreters Learning important features through propagating activation differences

Reference 40

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

A Tour of Convolutional Networks Guided by Linear Interpreters Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 41

Resolution
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Observation dc0fe1a5-034d-43f7-8f40-815ef9052314 · outbound

This paper cites Low-dimensional procedure for the characterization of human faces.

A Tour of Convolutional Networks Guided by Linear Interpreters Low-dimensional procedure for the characterization of human faces

Reference 42

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 8afff369-c9c2-4a58-b2f3-e0f2b80653d4 · outbound

This paper cites Introduction to linear algebra , volume 3.

A Tour of Convolutional Networks Guided by Linear Interpreters Introduction to linear algebra , volume 3

Reference 43

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 6bbd2e37-28e7-43a3-b580-a649230467a3 · outbound

This paper cites The discrete cosine transform.

A Tour of Convolutional Networks Guided by Linear Interpreters The discrete cosine transform

Reference 44

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 17e5bf2a-6ae6-4c1e-aa54-4820d74ea3f5 · outbound

This paper cites An introduction to matched filters.

A Tour of Convolutional Networks Guided by Linear Interpreters An introduction to matched filters

Reference 45

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation dbddb2f8-59d8-43cb-a032-0daa416762fc · outbound

This paper cites Spa- tial sine-wave responses of the human visual system.

A Tour of Convolutional Networks Guided by Linear Interpreters Spa- tial sine-wave responses of the human visual system

Reference 46

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 1abf44a6-35ff-4426-9977-81a0be6751c3 · outbound

This paper cites Accelerated stochastic power iteration.

A Tour of Convolutional Networks Guided by Linear Interpreters Accelerated stochastic power iteration

Reference 47

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 2a075868-a38e-47ba-90c0-0ee96f52bc82 · outbound

This paper cites Zeiler and Rob Fergus.

A Tour of Convolutional Networks Guided by Linear Interpreters Zeiler and Rob Fergus

Reference 48

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 2acb99d3-8bd6-4248-8298-9f4f97e4fbb1 · outbound

This paper cites Visual interpretability for deep learning: a survey.

A Tour of Convolutional Networks Guided by Linear Interpreters Visual interpretability for deep learning: a survey

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:25:44.128245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 906cb992-1395-4094-8275-8101c6a1e9d3 · outbound

This paper cites Interpretable basis decomposition for visual explanation.

A Tour of Convolutional Networks Guided by Linear Interpreters Interpretable basis decomposition for visual explanation

Reference 50

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation b615f6d5-2c49-4568-a0d7-fa5b25ee7512 · outbound

This paper cites Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.

A Tour of Convolutional Networks Guided by Linear Interpreters Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

Reference 51

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Pith citing papers

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