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Paper Citation Record · LEDGER

A Survey on Deep Neural Network Compression: Challenges, Overview, and Solutions

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2010.03954.

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

pith.paper-citation-record.v1
2010.03954 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 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-23T22:25:53.700079Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

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 2c91914e-7f92-440e-8fa5-c18975a1c307 · 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 Deep Neural Network Compression: Challenges, Overview, and Solutions

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-23T22:28:31.087196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-23T22:25:53.700079Z digest=sha256:bde39e9731998b661716f99efb5aa88c27b1ea50c9509cdd5f76486098730fdf

Observation 84dc2293-24f5-452a-8b73-9b00cc675a28 · 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 Deep Neural Network Compression: Challenges, Overview, and Solutions

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-18T08:41:07.936087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=pdf_text observed=2026-05-18T08:38:52.367887Z digest=sha256:c9e9d14ac190865b23bd3f81ffde8da0eb0541cd830e2bf3a1538179e69bc8f8