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Paper Citation Record · LEDGER

AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1805.08941.

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

pith.paper-citation-record.v1
1805.08941 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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:19:22.715493Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-23T03:35:21.024582Z

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 486e7e88-c8f4-4789-b218-a9ce6248a7de · inbound

Exploring Vision Neural Network Pruning via Screening Methodology cites this paper.

Exploring Vision Neural Network Pruning via Screening Methodology AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-05-23T03:35:21.027210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-23T03:33:15.015365Z digest=sha256:30a646eab2ba4eb6304142a091b3b88ad5ff66effdb770ba72cba42f0b7c9c7d

Observation 007764d2-cb82-4742-9a2d-9fc8fd889044 · inbound

Resource-Aware Neural Network Pruning Using Graph-based Reinforcement Learning cites this paper.

Resource-Aware Neural Network Pruning Using Graph-based Reinforcement Learning AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T10:19:22.715493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:19:22.715493Z digest=sha256:5357cc2c3f9a637459ae806a71bf19858b26d19fb54c2de9c037a3c4fd3bdbca