Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1809.00072.
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 2026-08-14T05:16:30.635065Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-25T12:25:48.060689Z
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 46a93fa0-1cef-408b-b9f9-034daa3a38d9 · inbound
X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks cites this paper.
X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T12:25:48.064173Z
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 09a4f3e0-c7a3-4fd7-b7e3-1daf12e6e4dc · inbound
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective cites this paper.
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:16:30.635065Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.