Paper Citation Record · LEDGER

A Survey on Methods and Theories of Quantized Neural Networks

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1808.04752.

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

pith.paper-citation-record.v1
1808.04752 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:09:57.928927Z

measured 1 of 1 external citation measurements

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

204
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 156d393b-f1bb-4d89-9044-bf93146146e0 · inbound

Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge cites this paper.

Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge A Survey on Methods and Theories of Quantized Neural Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T14:49:54.111981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3ef4ab8-40e3-4b09-93a9-cadecc5d39b4 · inbound

Reclaiming Residual Knowledge: A Novel Paradigm to Low-Bit Quantization cites this paper.

Reclaiming Residual Knowledge: A Novel Paradigm to Low-Bit Quantization A Survey on Methods and Theories of Quantized Neural Networks

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-23T22:28:31.077739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e5c4ba70-91ae-486f-824d-8ee8454d6a2f · inbound

Gradual Binary Search and Dimension Expansion : A general method for activation quantization in LLMs cites this paper.

Gradual Binary Search and Dimension Expansion : A general method for activation quantization in LLMs A Survey on Methods and Theories of Quantized Neural Networks

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-16T12:09:57.928927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daba71b3-0f6f-4866-9fa0-1811ceefa1ac · inbound

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs cites this paper.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs A Survey on Methods and Theories of Quantized Neural Networks

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:47.109355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c6e88e7-176a-4f57-bea6-0f2bf1708cb0 · inbound

Stochastic Weight Sharing for Bayesian Neural Networks cites this paper.

Stochastic Weight Sharing for Bayesian Neural Networks A Survey on Methods and Theories of Quantized Neural Networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:28.294739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95391eab-3e6b-43b7-a15e-4c353a57e777 · inbound

Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression cites this paper.

Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression A Survey on Methods and Theories of Quantized Neural Networks

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-18T08:41:07.987322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 050503bd-13f0-4104-bfac-2d5ee821281e · inbound

On the Expressive Power of Weight Quantization in Large Language Models cites this paper.

On the Expressive Power of Weight Quantization in Large Language Models A Survey on Methods and Theories of Quantized Neural Networks

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:19:44.223609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.