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Paper Citation Record · LEDGER

ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1811.01704.

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

pith.paper-citation-record.v1
1811.01704 v4

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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:30:12.655461Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T12:24:39.138559Z

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 a3f237a0-eb88-4255-929b-58f2234d3773 · inbound

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks cites this paper.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-14T13:30:12.655461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:30:12.655461Z digest=sha256:78a4f4ba93984051d82f93eafc393436e8fe48bc1335890e9039e93200e658e4

Observation 9e8fbddf-3c4f-49f3-b054-7b2d2dcf5a08 · inbound

Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training cites this paper.

Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-06-30T12:24:39.140403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-30T12:24:05.088824Z digest=sha256:ad55cf492656c5b40b21d09083970ab188eca59ccdefc03a386b159657fb8fa1