Paper Citation Record · LEDGER

Computing Linear Restrictions of Neural Networks

As of 15 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:1908.06214.

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

pith.paper-citation-record.v1
1908.06214 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:58:19.999583Z

measured 41 of 41 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 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

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 0c310d8d-a64d-49cd-a2ca-590a0bdb8e54 · outbound

This paper cites Deep learning.

Computing Linear Restrictions of Neural Networks Deep learning

Reference 1

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This paper cites Re- thinking the inception architecture for computer vision.

Computing Linear Restrictions of Neural Networks Re- thinking the inception architecture for computer vision

Reference 2

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Computing Linear Restrictions of Neural Networks Unresolved cited work

Reference 3

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Computing Linear Restrictions of Neural Networks BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 4

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This paper cites Deep neural network compression for aircraft collision avoidance systems.

Computing Linear Restrictions of Neural Networks Deep neural network compression for aircraft collision avoidance systems

Reference 5

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Observation d3f0c03f-d8ca-43d8-bdbe-a3779b5ae1c9 · outbound

This paper cites Goodfellow, and Rob Fergus.

Computing Linear Restrictions of Neural Networks Goodfellow, and Rob Fergus

Reference 6

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Observation 3bd594c1-83c4-4fd4-8649-63ccf350c236 · outbound

This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

Computing Linear Restrictions of Neural Networks Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 7

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Observation e0108cbd-7af4-426c-b113-505203893e39 · outbound

This paper cites Deepfool: A simple and accurate method to fool deep neural networks.

Computing Linear Restrictions of Neural Networks Deepfool: A simple and accurate method to fool deep neural networks

Reference 8

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This paper cites Audio adversarial examples: Targeted attacks on speech- to-text.

Computing Linear Restrictions of Neural Networks Audio adversarial examples: Targeted attacks on speech- to-text

Reference 9

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This paper cites Deep neural networks are easily fooled: High conﬁdence predictions for unrecognizable images.

Computing Linear Restrictions of Neural Networks Deep neural networks are easily fooled: High conﬁdence predictions for unrecognizable images

Reference 10

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Observation 826d9ac6-19f0-475f-8754-29815c4be2e6 · outbound

This paper cites Nori, and Antonio Criminisi.

Computing Linear Restrictions of Neural Networks Nori, and Antonio Criminisi

Reference 11

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Observation 20d100e3-9d1d-4d0b-9a05-1626819a077d · outbound

This paper cites Reluplex: An efﬁcient smt solver for verifying deep neural networks.

Computing Linear Restrictions of Neural Networks Reluplex: An efﬁcient smt solver for verifying deep neural networks

Reference 12

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This paper cites Formal veriﬁcation of piece-wise linear feed-forward neural networks.

Computing Linear Restrictions of Neural Networks Formal veriﬁcation of piece-wise linear feed-forward neural networks

Reference 13

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This paper cites Safety veriﬁcation of deep neural networks.

Computing Linear Restrictions of Neural Networks Safety veriﬁcation of deep neural networks

Reference 14

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Computing Linear Restrictions of Neural Networks Unresolved cited work

Reference 15

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Observation b9e58bc9-6998-4f0d-9293-53744dd7a9e5 · outbound

This paper cites Bunel, Ilker Turkaslan, Philip H.

Computing Linear Restrictions of Neural Networks Bunel, Ilker Turkaslan, Philip H

Reference 16

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Observation 0ef12b5e-1970-4eb7-a664-558114403d46 · outbound

This paper cites Boning, and Inderjit S.

Computing Linear Restrictions of Neural Networks Boning, and Inderjit S

Reference 17

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Observation 36d1f45a-1ae0-47a5-98ec-8fb99623523f · outbound

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Computing Linear Restrictions of Neural Networks Unresolved cited work

Reference 18

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Observation 90e70802-537f-40e8-9c34-8aa6ecff9001 · outbound

This paper cites Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness.

Computing Linear Restrictions of Neural Networks Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness

Reference 19

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This paper cites Zeiler and Rob Fergus.

Computing Linear Restrictions of Neural Networks Zeiler and Rob Fergus

Reference 20

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Observation 05e54330-6578-4713-af94-508f07529e6e · outbound

This paper cites Understanding Neural Networks Through Deep Visualization.

Computing Linear Restrictions of Neural Networks Understanding Neural Networks Through Deep Visualization

Reference 21

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This paper cites Network dissection: Quantifying interpretability of deep visual representations.

Computing Linear Restrictions of Neural Networks Network dissection: Quantifying interpretability of deep visual representations

Reference 22

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Observation 803d4b90-fd06-4e56-9e54-ff585623e403 · outbound

This paper cites How to explain individual classiﬁcation decisions.

Computing Linear Restrictions of Neural Networks How to explain individual classiﬁcation decisions

Reference 23

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Observation 36e728b3-4699-4e33-963b-05e7e8bca481 · outbound

This paper cites Not Just a Black Box: Learning Important Features Through Propagating Activation Differences.

Computing Linear Restrictions of Neural Networks Not Just a Black Box: Learning Important Features Through Propagating Activation Differences

Reference 24

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This paper cites Axiomatic attribution for deep networks.

Computing Linear Restrictions of Neural Networks Axiomatic attribution for deep networks

Reference 25

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This paper cites Cai, James Wexler, Fernanda B.

Computing Linear Restrictions of Neural Networks Cai, James Wexler, Fernanda B

Reference 26

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This paper cites A survey of methods for explaining black box models.

Computing Linear Restrictions of Neural Networks A survey of methods for explaining black box models

Reference 27

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Observation c312f6aa-5ddd-4844-ac9a-3ebb8ed05513 · outbound

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Computing Linear Restrictions of Neural Networks Himmelstein, Brett K

Reference 28

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Computing Linear Restrictions of Neural Networks Unresolved cited work

Reference 29

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Computing Linear Restrictions of Neural Networks Artiﬁcial intelligence in radiology

Reference 30

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Observation 4af98f04-bcf2-4c06-92e5-3a277c2667a0 · outbound

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Computing Linear Restrictions of Neural Networks Artiﬁcial intelligence in breast imaging: potentials and limitations

Reference 31

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Observation a0338566-bc4b-4b03-a492-9316adfc24ec · outbound

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Computing Linear Restrictions of Neural Networks Formal security analysis of neural networks using symbolic intervals

Reference 32

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This paper cites Differentiable abstract interpretation for provably robust neural networks.

Computing Linear Restrictions of Neural Networks Differentiable abstract interpretation for provably robust neural networks

Reference 33

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This paper cites Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations.

Computing Linear Restrictions of Neural Networks Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations

Reference 34

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This paper cites Rosenfeld, and Taylor T.

Computing Linear Restrictions of Neural Networks Rosenfeld, and Taylor T

Reference 35

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This paper cites Output range analysis for deep feedforward neural networks.

Computing Linear Restrictions of Neural Networks Output range analysis for deep feedforward neural networks

Reference 36

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Computing Linear Restrictions of Neural Networks Interpretable Deep Learning in Drug Discovery

Reference 37

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This paper cites https://github.com/eth-sri/ eran, 2019.

Computing Linear Restrictions of Neural Networks https://github.com/eth-sri/ eran, 2019

Reference 38

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Observation ad153ad1-891b-4cbf-9609-af3f9675bdf6 · outbound

This paper cites How Important Is a Neuron?.

Computing Linear Restrictions of Neural Networks How Important Is a Neuron?

Reference 39

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This paper cites A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples.

Computing Linear Restrictions of Neural Networks A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples

Reference 40

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Observation e4181f1e-1277-4c80-bde9-8d6044db3b9d · outbound

This paper cites matter of seconds.

Computing Linear Restrictions of Neural Networks matter of seconds

Reference 41

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Pith citing papers

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