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Paper Citation Record · LEDGER

Learning Discrete Weights Using the Local Reparameterization Trick

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1710.07739.

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

pith.paper-citation-record.v1
1710.07739 v3

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 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:32:44.309324Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T12:00:59.309256Z

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 6e460636-3f39-4e66-bcb6-4e4c8ff0faa3 · inbound

VQ4ALL: Efficient Neural Network Representation via a Universal Codebook cites this paper.

VQ4ALL: Efficient Neural Network Representation via a Universal Codebook Learning Discrete Weights Using the Local Reparameterization Trick

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-11T19:32:44.309324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:32:44.309324Z digest=sha256:e792ddd66d8fd079f0ccb4ef49c7b827a3cf6ef7899978ed1d175d337d91fc34

Observation 552cc453-78b4-483b-98b9-cd62bdae8f0c · inbound

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning cites this paper.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Learning Discrete Weights Using the Local Reparameterization Trick

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-08-08T12:00:59.314491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-08T12:00:53.838212Z digest=sha256:e75fbca2671bef8617224109cb9e20c094d92f6d047ed495f1d8972858f7108f