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Paper Citation Record · LEDGER

Quantization for Rapid Deployment of Deep Neural Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1810.05488.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1810.05488 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:18:08.780701Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T16:42:12.606484Z

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 38f74cb6-740b-4386-ac78-58367af3f543 · inbound

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices cites this paper.

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices Quantization for Rapid Deployment of Deep Neural Networks

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T10:18:08.780701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:18:08.780701Z digest=sha256:7da25bc9c566fb2cc2fa21428dd166d7e53a678f913f3e0094d1d07e4e85d6c8

Observation 7fe30382-856e-4975-86aa-cc15ff806964 · inbound

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization cites this paper.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Quantization for Rapid Deployment of Deep Neural Networks

Reference 29

Resolution
verified exact
local_arxiv, observed 2026-08-06T16:42:12.611707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T16:42:12.031599Z digest=sha256:a8e3131fa9beb505ca5cf34fd8c761f78a7a1725ed8bbf9a3f814d4bc83e9541