Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving performance of recurrent neural network with relu nonlinearity
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1511.03771.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T11:42:29.884788Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-01T14:15:47.531994Z
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 3b60d986-b0e9-420f-bad6-3f04ca6fc86a · inbound
RNNs Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients? cites this paper.
RNNs Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients? Improving performance of recurrent neural network with relu nonlinearity
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T11:42:29.884788Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d54ab3fd-a249-4fbe-babf-ca54ba278535 · inbound
Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications cites this paper.
Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications Improving performance of recurrent neural network with relu nonlinearity
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T06:37:26.809331Z
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 24fae74e-505c-4fee-b327-b32981dfcd53 · inbound
Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications cites this paper.
Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications Improving performance of recurrent neural network with relu nonlinearity
Reference 5
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-01T14:15:47.533268Z
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.