Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1807.10029.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-29T05:08:23.176105Z
A source-named dated measurement, never combined with another source.
Source: pith, 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
45
pith, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 5513a057-a47c-45f4-8852-8b04a948abad · inbound
Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic cites this paper.
Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
Reference 69
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T13:45:53.375457Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b21212da-a70a-4a09-8dc8-29b05ffbcd5e · inbound
Evolutionary fine tuning of quantized convolution-based deep learning models cites this paper.
Evolutionary fine tuning of quantized convolution-based deep learning models LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T06:26:27.278994Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 067b2e52-99a6-4d69-9dc9-2f4eb9b4e065 · inbound
Layerwise Progressive Freezing: A Training Scaffold for Depth-Scalable Binary Networks cites this paper.
Layerwise Progressive Freezing: A Training Scaffold for Depth-Scalable Binary Networks LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T05:13:06.478875Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.