Paper Citation Record · LEDGER

AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1902.09689.

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

pith.paper-citation-record.v1
1902.09689 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:23:10.289637Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:27:08.377186Z

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 7faac380-5a83-43b0-896c-265d1f182091 · inbound

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases cites this paper.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T20:23:10.289637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6510ce91-6ec5-4d0f-9287-1c8929c19fc5 · inbound

Analog classical simulation of closed quantum systems cites this paper.

Analog classical simulation of closed quantum systems AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-08T15:59:24.342781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3982352-d0f7-46c0-b14f-7a2fff41db7c · inbound

Temporal Chunking Enhances Recognition of Implicit Sequential Patterns cites this paper.

Temporal Chunking Enhances Recognition of Implicit Sequential Patterns AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T12:14:21.509192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 982fa725-cb70-4d15-9d92-ca4f5eea2ce7 · inbound

Temporal horizons in forecasting: a performance-learnability trade-off cites this paper.

Temporal horizons in forecasting: a performance-learnability trade-off AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:02:56.729571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85581ed9-7284-4859-adc2-c471a900c7c8 · inbound

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum cites this paper.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Reference 65

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:27:08.380777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8758c36e-d8a7-48a6-8166-0562399aa850 · inbound

Universal Approximation Theorems for Dynamical Systems with Infinite-Time Horizon Guarantees cites this paper.

Universal Approximation Theorems for Dynamical Systems with Infinite-Time Horizon Guarantees AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T03:21:48.759368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.