Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Training Binary Neural Networks with Real-to-Binary Convolutions
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2003.11535.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T20:18:10.499300Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-13T17:28:02.185592Z
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 756a7a0f-f1b5-41b7-96e3-62cc7b3c6d79 · inbound
BiDM: Pushing the Limit of Quantization for Diffusion Models cites this paper.
BiDM: Pushing the Limit of Quantization for Diffusion Models Training Binary Neural Networks with Real-to-Binary Convolutions
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:18:10.499300Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f6de549d-7e05-460f-a7cb-9d6e02067738 · inbound
MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models cites this paper.
MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models Training Binary Neural Networks with Real-to-Binary Convolutions
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T14:55:03.453244Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 06d67a0a-7272-4a6c-adb2-7c4b79da9b16 · inbound
BWTA: Accurate and Efficient Binarized Transformer by Algorithm-Hardware Co-design cites this paper.
BWTA: Accurate and Efficient Binarized Transformer by Algorithm-Hardware Co-design Training Binary Neural Networks with Real-to-Binary Convolutions
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T17:28:02.186940Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.