Paper Citation Record · LEDGER

Sobolev Training for Neural Networks

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1706.04859.

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

pith.paper-citation-record.v1
1706.04859 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T23:53:02.118884Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T23:57:27.965532Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3be29862-c4db-4368-9141-28567cb72fae · inbound

KANs need curvature: penalties for compositional smoothness cites this paper.

KANs need curvature: penalties for compositional smoothness Sobolev Training for Neural Networks

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:05:36.394951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1c01a776-5faf-48d7-bd81-1ccc35af79bb · inbound

Real-time virtual circuits for plasma shape control via neural network emulators cites this paper.

Real-time virtual circuits for plasma shape control via neural network emulators Sobolev Training for Neural Networks

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-15T14:30:03.639492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f16fb640-d763-4fd9-9fe0-ae4febbbc24a · inbound

Real-time virtual circuits for plasma shape control via neural network emulators cites this paper.

Real-time virtual circuits for plasma shape control via neural network emulators Sobolev Training for Neural Networks

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:57:27.966737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5c30aa54-9cf9-40b5-a3c2-4937c36b1320 · inbound

Layer-wise Derivative Controlled Networks cites this paper.

Layer-wise Derivative Controlled Networks Sobolev Training for Neural Networks

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-19T15:22:37.380047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0ea589a7-982a-42d8-902e-eddb8dacc36c · inbound

ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks cites this paper.

ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks Sobolev Training for Neural Networks

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-06-30T15:14:46.775882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4aeb3cf1-c2c4-4f8b-843e-9a5a1a0bbaf6 · inbound

LEIA: Learned Environment for Interactive Architected Materials cites this paper.

LEIA: Learned Environment for Interactive Architected Materials Sobolev Training for Neural Networks

Reference 43

Resolution: metadata mismatch; local_arxiv, observed 2026-06-29T13:43:29.070401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b9c568e0-4f09-43d9-b27c-91060410658a · inbound

Layer-wise Derivative Controlled Networks Achieve Competitive Accuracy and Gradient Stability Across Data Regimes cites this paper.

Layer-wise Derivative Controlled Networks Achieve Competitive Accuracy and Gradient Stability Across Data Regimes Sobolev Training for Neural Networks

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-02T20:17:22.532077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.