Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2301.13330.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T12:23:44.678380Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation a97fc097-365a-42d4-93a6-70764ba4c225 · inbound
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs cites this paper.
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T12:23:44.678380Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ce3d902-1ce3-4c39-b5a9-06fa93b0cb5e · inbound
A probabilistic framework for dynamic quantization cites this paper.
A probabilistic framework for dynamic quantization Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:12:21.628322Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 00f3bc2d-5ede-4a89-8e94-f1042a1a2e48 · inbound
SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures cites this paper.
SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T02:33:04.935304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.