Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2103.13813.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:26:08.020924Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-28T19:02:34.378411Z
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 449698d9-a170-4934-87d5-ef8d1482fd1c · inbound
ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks cites this paper.
ObfusBFA: A Holistic Approach to Safeguarding DNNs from Different Types of Bit-Flip Attacks RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:26:08.020924Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f6369916-592a-4770-b51b-da796cd229ee · inbound
Quantum Tunneling-Aware Machine Learning: Physics-Derived Noise Models for Robust Deployment cites this paper.
Quantum Tunneling-Aware Machine Learning: Physics-Derived Noise Models for Robust Deployment RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T19:02:34.379709Z
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.