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Paper Citation Record · LEDGER

A Practical Sparse Approximation for Real Time Recurrent Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2006.07232.

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

pith.paper-citation-record.v1
2006.07232 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 2026-08-07T05:51:43.153199Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T19:43:23.065000Z

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 6692d611-ba6e-4166-9796-3ee6081b8dcb · inbound

Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers cites this paper.

Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers A Practical Sparse Approximation for Real Time Recurrent Learning

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-23T19:43:23.072853Z

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=pdf_text observed=2026-05-23T19:42:45.987188Z digest=sha256:feb3e55c0c5f5842bd9a7b3f84befce500f96274b17ae33fd83381e9f553365f

Observation d531cbd2-95ec-4fd0-8c2c-91be2a47d0bc · inbound

Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example cites this paper.

Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example A Practical Sparse Approximation for Real Time Recurrent Learning

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-07T05:51:43.153199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:51:43.153199Z digest=sha256:c5ae61260bbc8a1f4508d730d8df2b2da76e90adce950b54df5d41d4b69f5e8f

Observation e72b4078-184d-4107-977e-11cda45fc4d3 · inbound

The Global Empirical NTK: Self-Referential Bias and Dimensionality of Gradient Descent Learning cites this paper.

The Global Empirical NTK: Self-Referential Bias and Dimensionality of Gradient Descent Learning A Practical Sparse Approximation for Real Time Recurrent Learning

Reference 104

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T03:01:18.493006Z

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=2026-05-12T02:57:57.097441Z digest=sha256:738615021ea6056c733eb988aba93d889ffa5655973fb4c06510a87d8010475b

Observation ef4d38fb-1b2c-416c-ba2c-5d245884fcd7 · inbound

State-Space NTK Collapse Near Bifurcations cites this paper.

State-Space NTK Collapse Near Bifurcations A Practical Sparse Approximation for Real Time Recurrent Learning

Reference 104

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T21:19:28.965463Z

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=2026-05-14T21:03:49.445965Z digest=sha256:72d9577d2e8e76760e5ba8f90607cddebf1aab561f1a96238d3a70e3e988ae7e

Observation 72b82647-fe2e-41a6-a433-c989798fea99 · inbound

Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning cites this paper.

Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning A Practical Sparse Approximation for Real Time Recurrent Learning

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-20T23:29:13.190992Z

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=pdf_text observed=2026-05-20T23:26:23.989545Z digest=sha256:b8d19850a0c57654ebbff5a34d3fb5611efb0e7f73359b1660384e414955819b