Paper Citation Record · LEDGER

Adversarially robust generalization theory via Jacobian regularization for deep neural networks

As of 18 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2412.12449.

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

pith.paper-citation-record.v1
2412.12449 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:11:00.923865Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

18 of 18 outbound references displayed

verified exact2
verified fuzzy1
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9b381676-cc8d-4dff-a888-6e96ff846729 · outbound

This paper cites Robust Learning with Jacobian Regularization.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Robust Learning with Jacobian Regularization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.859448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b304c945-7f4f-496e-b0d9-dad3f25b7a75 · outbound

This paper cites 2015 IEEE International Confer ence on Data Mining , 301–309.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks 2015 IEEE International Confer ence on Data Mining , 301–309

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T14:11:01.307805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4588c51a-7d4d-45a8-8b03-c48f88b2ced4 · outbound

This paper cites A vector-contraction inequality for Rademacher complexities.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks A vector-contraction inequality for Rademacher complexities

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.886510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df711080-9d01-4850-96b7-6544f5be65e8 · outbound

This paper cites Sensitivity and Generalization in Neural Networks: an Empirical Study.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Sensitivity and Generalization in Neural Networks: an Empirical Study

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.895993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d713f195-7cfe-40dd-800b-3cbb8cfacf16 · outbound

This paper cites Adversarially Robust Generalization Requires More Data.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Adversarially Robust Generalization Requires More Data

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.900313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30a88216-01f6-4704-9de0-a09b0b1b1285 · outbound

This paper cites IEEE Transactions on Signal Proce ssing 65, 4265–4280.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks IEEE Transactions on Signal Proce ssing 65, 4265–4280

Reference 14

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T14:11:01.092161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8137ce8a-46b7-4f04-82f2-e315904209c1 · outbound

This paper cites Gradient Regularization Improves Accuracy of Discriminative Models.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Gradient Regularization Improves Accuracy of Discriminative Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.914536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71930daf-4f7f-47fd-9897-3a7fa86beb56 · outbound

This paper cites Bridging the Gap: Rademacher Complexity in Robust and Standard Generalization.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Bridging the Gap: Rademacher Complexity in Robust and Standard Generalization

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:11:00.964298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e6efd0e4-ec27-4eaf-aace-623df5be994d · outbound

This paper cites Intriguing properties of neural networks.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Intriguing properties of neural networks

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.909626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f51c19d2-8c4d-405b-b985-70f95a2fd56d · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Explaining and Harnessing Adversarial Examples

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.849981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 191a636f-c511-451a-a96a-bd48f2dda7de · outbound

This paper cites Learning with a Strong Adversary.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Learning with a Strong Adversary

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.864761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5573d053-1961-460f-aa0d-0cb36476bfca · outbound

This paper cites Adversarial examples in the physical world.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Adversarial examples in the physical world

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.874022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea41ef1d-3b93-4bcc-a0d8-2d956f12d83e · outbound

This paper cites A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.890576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af7d5549-1d7d-4a90-9ded-a3a4724ad082 · outbound

This paper cites Adversarial Risk Bounds via Function Transformation.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Adversarial Risk Bounds via Function Transformation

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.869448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0ca1fab-7c99-4dbf-9910-5212aed84a82 · outbound

This paper cites Jacobian Adversarially Regularized Networks for Robustness.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Jacobian Adversarially Regularized Networks for Robustness

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.844786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72ed8314-01b9-4a9a-a721-4db9585c649e · outbound

This paper cites Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.854464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96f730d2-6f62-4803-959b-57ea9a2512ba · outbound

This paper cites Adversarial Rademacher Complexity of Deep Neural Networks.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Adversarial Rademacher Complexity of Deep Neural Networks

Reference 2022

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:11:00.983825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 14661ff1-9333-4161-b1fa-1136f830efd5 · outbound

This paper cites Pattern Recognition 145 , 109902.

Adversarially robust generalization theory via Jacobian regularization for deep neural networks Pattern Recognition 145 , 109902

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T14:11:00.878108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.