Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1808.05779.
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 2026-08-14T13:30:12.566504Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T06:26:27.272558Z
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 19b40e55-6add-4913-82a2-dd569eed5c5d · inbound
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks cites this paper.
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T13:30:12.566504Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8d4a21af-f7f7-49cc-8961-b5b9ca6a6223 · inbound
Efficient Deep Neural Networks cites this paper.
Efficient Deep Neural Networks Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
Reference 75
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:19:53.611892Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b15bb311-8692-4a5d-9967-a099d9c2c14e · inbound
Evolutionary fine tuning of quantized convolution-based deep learning models cites this paper.
Evolutionary fine tuning of quantized convolution-based deep learning models Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T23:13:05.041516Z
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.