Paper Citation Record · LEDGER

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty

As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2606.23942.

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

pith.paper-citation-record.v1
2606.23942 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T08:52:08.585216Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

19 of 19 outbound references displayed

verified exact4
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9b399b76-7d38-4c0f-b485-d040ad4d2b3c · outbound

This paper cites Contractive auto-encoders: Explicit invariance during feature extraction,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Contractive auto-encoders: Explicit invariance during feature extraction,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1a6e63c-26ef-48d1-bff8-5e97720fc962 · outbound

This paper cites Improving generalization performance using double backpropagation,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Improving generalization performance using double backpropagation,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf2ec1d5-f68c-4014-8702-8fa14d2a5fbc · outbound

This paper cites Robust Learning with Jacobian Regularization.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Robust Learning with Jacobian Regularization

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:29:44.667067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1509fdb7-9ffe-4c8b-82a3-f40c0b765035 · outbound

This paper cites On the history of Lie brackets, crossed modules, and Lie-Rinehart algebras.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty On the history of Lie brackets, crossed modules, and Lie-Rinehart algebras

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:29:44.661495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 95bc839e-0d1c-467c-9473-9b1b4dd73400 · outbound

This paper cites Spectral normal- ization for generative adversarial networks,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Spectral normal- ization for generative adversarial networks,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfd9f544-42be-4f10-b3c2-0c49cc328d1c · outbound

This paper cites Large scale GAN training for high fidelity natural image synthesis,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Large scale GAN training for high fidelity natural image synthesis,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd90838e-b1f0-42ae-a64a-a0d987f9385a · outbound

This paper cites Dropout: A simple way to prevent neural networks from over- fitting,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Dropout: A simple way to prevent neural networks from over- fitting,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cca2c4b0-72f9-447c-bdba-571ffbf76d4a · outbound

This paper cites A simple weight decay can improve general- ization,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty A simple weight decay can improve general- ization,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83149072-331d-44c0-b412-912b6a2d9699 · outbound

This paper cites Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84de5f42-067a-4850-a753-6655a74b793d · outbound

This paper cites Multilayer feedforward networks are universal approximators,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Multilayer feedforward networks are universal approximators,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8821b0a6-621f-484a-84a8-2bd137e3357c · outbound

This paper cites Searching for Activation Functions.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Searching for Activation Functions

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-04T10:29:44.664253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2be12dbb-e292-49c4-b3d8-83b78987698d · outbound

This paper cites Gaussian Error Linear Units (GELUs).

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Gaussian Error Linear Units (GELUs)

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-04T10:29:44.658504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation afd08e9e-c714-499b-b29c-0f8f0a5d570a · outbound

This paper cites Attention is all you need,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Attention is all you need,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6570f549-c7be-4931-8756-1cca7312e2d4 · outbound

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

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty An image is worth 16×16 words: Transformers for image recognition at scale,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc342574-b540-4cd4-89ea-b1d49f429d63 · outbound

This paper cites Rademacher and Gaussian complexities: Risk bounds and structural results,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Rademacher and Gaussian complexities: Risk bounds and structural results,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02714cc7-ab5d-4de4-9a77-c2f0864a7225 · outbound

This paper cites Understand- ing deep learning (still) requires rethinking generalization,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Understand- ing deep learning (still) requires rethinking generalization,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 241ed906-4b68-4363-a493-d49eb1f6276a · outbound

This paper cites Robust large margin deep neural networks,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Robust large margin deep neural networks,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a0ed3d6-2aed-4a0f-9eaf-b5460c69ebea · outbound

This paper cites Fixup initialization: Residual learning without normalization,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Fixup initialization: Residual learning without normalization,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 102b5247-166b-4969-a954-2d3a1fcc073a · outbound

This paper cites Accounting for variance in machine learning benchmarks,.

DREG: A Layer-Wise Jacobian Regularization as a General-Purpose Penalty Accounting for variance in machine learning benchmarks,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-26T08:52:08.585216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.