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Paper Citation Record · LEDGER

Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1612.06052.

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

pith.paper-citation-record.v1
1612.06052 v2

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 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:17:36.329745Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T23:25:54.832031Z

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 e8f64916-af6f-4a45-ad38-533c90293ad4 · inbound

GDRQ: Group-based Distribution Reshaping for Quantization cites this paper.

GDRQ: Group-based Distribution Reshaping for Quantization Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-14T15:17:36.329745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T15:17:36.329745Z digest=sha256:6927fdf49c1174d9ec6cd2fd1a2aa3bf9241e7966fbe5f1128ca918f6bc7cdfe

Observation 5ee09635-d575-4d3c-8ecd-3ef489f995c9 · inbound

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization cites this paper.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization Quantization and Training of Low Bit-Width Convolutional Neural Networks for Object Detection

Reference 29

Resolution
verified exact
local_arxiv, observed 2026-08-09T23:25:54.839680Z

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.

source=pdf_text observed=2026-08-09T23:25:54.739234Z digest=sha256:7cd83dc975dad8ebdac19597f6adbd93302dad3b60648498a78f7b101003b3ec