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Paper Citation Record · LEDGER

Improving Energy Conserving Descent for Machine Learning: Theory and Practice

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2306.00352.

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

pith.paper-citation-record.v1
2306.00352 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:10:03.869819Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T05:39:40.981895Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • 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 69b14faa-c288-4afe-910f-7157d7caa7ff · inbound

Machine Learning Gravity Compactifications on Negatively Curved Manifolds cites this paper.

Machine Learning Gravity Compactifications on Negatively Curved Manifolds Improving Energy Conserving Descent for Machine Learning: Theory and Practice

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-10T23:10:03.869819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:10:03.869819Z digest=sha256:01d98c89c7d7f284d005b227412123fef73abc75f33b1e82296745630f7b8223

Observation 2885438e-9763-4681-84b7-280dd7cd5746 · inbound

Optimizers for Stabilizing Likelihood-free Inference cites this paper.

Optimizers for Stabilizing Likelihood-free Inference Improving Energy Conserving Descent for Machine Learning: Theory and Practice

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-09T23:37:30.060469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T23:37:30.060469Z digest=sha256:fb8f0d8e701ef90e997f47a17ad28178c90227597f61666e780dc035a0c74405

Observation e7dd9030-904a-43ad-bbaf-cc52c7cafda4 · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Improving Energy Conserving Descent for Machine Learning: Theory and Practice

Reference 165

Resolution
verified exact
arxiv_id, observed 2026-07-04T05:39:40.984222Z

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.

source=arxiv_source observed=2026-06-26T15:35:51.654392Z digest=sha256:69c91d4aa9ea04a28a7f70f09faab849d546389b93b81521d0d1da680bff8303

Observation 1320fb97-8085-4981-90ae-b38f38ff97aa · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Improving Energy Conserving Descent for Machine Learning: Theory and Practice

Reference 165

Resolution
verified exact
arxiv_id, observed 2026-07-02T21:57:25.664868Z

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.

source=arxiv_source observed=2026-07-02T21:51:13.457071Z digest=sha256:948c159e2b8aa243347929f2e8c94307736b68e955769c7c1309d26f54b2a644

Observation a1c70d17-2c3f-4bca-ad30-c5f58fb7994d · inbound

Pre-Strings Lectures on Artificial Intelligence cites this paper.

Pre-Strings Lectures on Artificial Intelligence Improving Energy Conserving Descent for Machine Learning: Theory and Practice

Reference 20

Resolution
unresolved
no resolver link, observed 2026-07-12T06:14:03.658427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T06:14:03.658427Z digest=sha256:f656be730146ac7f7104ad98b7fb7d44326f218df07eaaa5b21f24c8e5e909ad