Paper Citation Record · LEDGER

Investigating Decision Boundaries of Trained Neural Networks

As of 21 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:1908.02802.

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

pith.paper-citation-record.v1
1908.02802 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:36:42.055701Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T13:22:37.107679Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T13:24:53.306749Z

Reference resolution

23 of 23 outbound references displayed

verified exact1
verified fuzzy17
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad783b83-56ab-4484-90a8-b4d96c9bec0f · outbound

This paper cites Large margin deep networks for classification.

Investigating Decision Boundaries of Trained Neural Networks Large margin deep networks for classification

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.386953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 09073253-9da3-4386-9301-d463bca980d5 · outbound

This paper cites The robustness of deep networks: A geometrical perspective.

Investigating Decision Boundaries of Trained Neural Networks The robustness of deep networks: A geometrical perspective

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.374919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 24d20aff-c015-4694-9193-138af9aca2e6 · outbound

This paper cites Empirical study of the topology and geometry of deep networks.

Investigating Decision Boundaries of Trained Neural Networks Empirical study of the topology and geometry of deep networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.362121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a3b27eef-10bd-48e5-b85c-febf5c708f1c · outbound

This paper cites Matrix Computations.

Investigating Decision Boundaries of Trained Neural Networks Matrix Computations

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.349249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 89e11a19-2fcd-454d-be13-157d01614c89 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Investigating Decision Boundaries of Trained Neural Networks Explaining and Harnessing Adversarial Examples

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T14:36:41.978006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45b52447-6996-4655-b868-4b577c48b984 · outbound

This paper cites Formal guarantees on the robustness of a classifier against adversarial manipulation.

Investigating Decision Boundaries of Trained Neural Networks Formal guarantees on the robustness of a classifier against adversarial manipulation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.336762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d1a2985f-7c30-4d7e-b21b-ee827653a502 · outbound

This paper cites Adversarial Examples Are Not Bugs, They Are Features.

Investigating Decision Boundaries of Trained Neural Networks Adversarial Examples Are Not Bugs, They Are Features

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T14:36:41.988210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9aadc593-a1d0-4df7-85cb-a70c4c59b7c0 · outbound

This paper cites With friends like these, who needs adversaries? In Advances in Neural Information Processing Systems (NeurIPS 2018), pages 10749--10759, 2018.

Investigating Decision Boundaries of Trained Neural Networks With friends like these, who needs adversaries? In Advances in Neural Information Processing Systems (NeurIPS 2018), pages 10749--10759, 2018

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.324280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c9d34055-7c65-49e7-83b4-09f70569f126 · outbound

This paper cites Predicting the generalization gap in deep networks with margin distributions.

Investigating Decision Boundaries of Trained Neural Networks Predicting the generalization gap in deep networks with margin distributions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.312017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 93716db6-7eb1-4565-8bcf-6caf82f9e445 · outbound

This paper cites an unresolved cited work.

Investigating Decision Boundaries of Trained Neural Networks Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-14T14:36:42.299903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6206c877-1a6e-49da-9622-351837c5861c · outbound

This paper cites An introduction to computing with neural nets.

Investigating Decision Boundaries of Trained Neural Networks An introduction to computing with neural nets

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.286498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 42605eb8-f0fe-4677-a3ef-62d6eb2a45c0 · outbound

This paper cites Margin maximization for robust classification using deep learning.

Investigating Decision Boundaries of Trained Neural Networks Margin maximization for robust classification using deep learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.273850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 94ed83fd-32ac-4b4c-b06a-bacd22b26055 · outbound

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

Investigating Decision Boundaries of Trained Neural Networks Deepfool: a simple and accurate method to fool deep neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.261675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 364cbd2d-e823-4a88-ae14-6e8d6b4d36c8 · outbound

This paper cites Exploring generalization in deep learning.

Investigating Decision Boundaries of Trained Neural Networks Exploring generalization in deep learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.248732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 52706a77-8d32-4085-bc81-904847ad3603 · outbound

This paper cites Why should I trust you?: Explaining the predictions of any classifier.

Investigating Decision Boundaries of Trained Neural Networks Why should I trust you?: Explaining the predictions of any classifier

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.234691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5950ad7d-cd26-4407-a7c3-bf92c248c702 · outbound

This paper cites A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance.

Investigating Decision Boundaries of Trained Neural Networks A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T14:36:42.026061Z

Source-reported events for the cited work

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Observation d7bec65b-9162-4823-af8b-cc1c4caf69a5 · outbound

This paper cites Actionable recourse in linear classification.

Investigating Decision Boundaries of Trained Neural Networks Actionable recourse in linear classification

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.220466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 893143c3-e2b1-43c2-b1ae-1c211c6a3c6d · outbound

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

Investigating Decision Boundaries of Trained Neural Networks A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T14:36:42.034948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 379dcdc8-9f68-4824-a68f-633626c99429 · outbound

This paper cites Robustness may be at odds with accuracy.

Investigating Decision Boundaries of Trained Neural Networks Robustness may be at odds with accuracy

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.207188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1a493205-2109-4233-b560-2f102ccbc593 · outbound

This paper cites A tutorial on spectral clustering.

Investigating Decision Boundaries of Trained Neural Networks A tutorial on spectral clustering

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.194524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a8ac3c08-addd-4505-93c0-8cfba18df9bd · outbound

This paper cites On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming.

Investigating Decision Boundaries of Trained Neural Networks On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.180298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 008bfb9d-0daf-4ebe-9ae7-88a6d686fe07 · outbound

This paper cites Counterfactual explanations without opening the black box: Automated decisions and the GDPR.

Investigating Decision Boundaries of Trained Neural Networks Counterfactual explanations without opening the black box: Automated decisions and the GDPR

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:36:42.166492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 34f0fc81-376c-4705-ac5b-4d3a393c4ea4 · outbound

This paper cites Interpreting Neural Networks Using Flip Points.

Investigating Decision Boundaries of Trained Neural Networks Interpreting Neural Networks Using Flip Points

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:36:42.098785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation db2f097a-5a13-4fc5-a2cf-95b4cfd2abc2 · inbound

Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary cites this paper.

Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary Investigating Decision Boundaries of Trained Neural Networks

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:24:53.309543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.