Paper Citation Record · LEDGER

How to train your neural ODE: the world of Jacobian and kinetic regularization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2002.02798.

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

pith.paper-citation-record.v1
2002.02798 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:07:24.635859Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T22:23:48.144112Z

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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 032b4c94-cd72-4c3a-b153-58651d1e1284 · inbound

Deep Potential: Recovering the gravitational potential and local pattern speed in the solar neighborhood with GDR3 using normalizing flows cites this paper.

Deep Potential: Recovering the gravitational potential and local pattern speed in the solar neighborhood with GDR3 using normalizing flows How to train your neural ODE: the world of Jacobian and kinetic regularization

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T20:07:24.635859Z

Source-reported events for the cited work

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Observation 83e33c44-306b-435d-8d9f-756ffd829098 · inbound

Generalization Bound for a General Class of Neural Ordinary Differential Equations cites this paper.

Generalization Bound for a General Class of Neural Ordinary Differential Equations How to train your neural ODE: the world of Jacobian and kinetic regularization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T16:14:10.310715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a3f3033-e7c9-49b6-b2b6-6624fd343924 · inbound

A Kinetic Energy Perspective of Flow Matching cites this paper.

A Kinetic Energy Perspective of Flow Matching How to train your neural ODE: the world of Jacobian and kinetic regularization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T03:31:36.851800Z

Source-reported events for the cited work

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Observation c15a701f-a633-412a-833f-c99f41923a02 · inbound

IRIS: A Real-World Benchmark for Inverse Recovery and Identification of Physical Dynamic Systems from Monocular Video cites this paper.

IRIS: A Real-World Benchmark for Inverse Recovery and Identification of Physical Dynamic Systems from Monocular Video How to train your neural ODE: the world of Jacobian and kinetic regularization

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-13T23:47:18.378887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2345d417-92cc-41c3-aa41-f891409f8256 · inbound

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots cites this paper.

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots How to train your neural ODE: the world of Jacobian and kinetic regularization

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:06:26.969231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0ac32b6b-43e5-4307-89d6-724e48d59c24 · inbound

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots cites this paper.

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots How to train your neural ODE: the world of Jacobian and kinetic regularization

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:52:59.156267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f264fd25-6239-4ebd-aa44-c47a38089c5a · inbound

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots cites this paper.

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots How to train your neural ODE: the world of Jacobian and kinetic regularization

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:23:48.146779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b5a1b70f-fedb-4519-b355-5ec4bfee4eca · inbound

Convex Relaxations for the Optimization of Markov Processes cites this paper.

Convex Relaxations for the Optimization of Markov Processes How to train your neural ODE: the world of Jacobian and kinetic regularization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-13T03:08:01.989422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.