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Paper Citation Record · LEDGER

Expressive power of recurrent neural networks

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1711.00811.

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

pith.paper-citation-record.v1
1711.00811 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:11:46.032703Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T23:53:12.254478Z

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 239fb816-a744-4ed0-9333-ee38cecba0b5 · inbound

The impact of allocation strategies in subset learning on the expressive power of neural networks cites this paper.

The impact of allocation strategies in subset learning on the expressive power of neural networks Expressive power of recurrent neural networks

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-08T16:11:46.032703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T16:11:46.032703Z digest=sha256:cc974ceeac92f6c0da4e1918be89b4f616cc8b3fbd9c777b857ea7603c8e7c1f

Observation ea5732e2-a6fc-4895-b46f-d0a6148a24bd · inbound

A Scalable Factorization Approach for High-Order Structured Tensor Recovery cites this paper.

A Scalable Factorization Approach for High-Order Structured Tensor Recovery Expressive power of recurrent neural networks

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:53:12.258048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:53:10.412443Z digest=sha256:e4faf85513756fbe7493cbde0080b85bd20d865fea80f0fbf9ce133ddff106e1

Observation df90365b-ea43-4222-986f-4b7a0ba06ac8 · inbound

Reassessing Muon for Matrix Factorization cites this paper.

Reassessing Muon for Matrix Factorization Expressive power of recurrent neural networks

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-02T05:49:17.903439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T05:49:17.903439Z digest=sha256:8db65f613f526fc6b12b6c28b14cb526e1bf9eb4f0af382bf17e0b6399ec54b9

Observation 95192450-50da-42f0-859e-d1a086ab72c3 · inbound

Reassessing Muon for Matrix Factorization cites this paper.

Reassessing Muon for Matrix Factorization Expressive power of recurrent neural networks

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-04T04:22:54.780874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T04:22:54.780874Z digest=sha256:b49156b1f6ae6d6cc7c09b05126c22add0054bd5dfe0b8460b8a4007dbd6ade0