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Paper Citation Record · LEDGER

DyNet: Dynamic Convolution for Accelerating Convolutional Neural Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2004.10694.

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

pith.paper-citation-record.v1
2004.10694 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:32:23.728523Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:32:23.863415Z

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 ebbf5a88-999f-466c-9327-c72d24567995 · inbound

Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations cites this paper.

Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations DyNet: Dynamic Convolution for Accelerating Convolutional Neural Networks

Reference 56

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:32:23.869720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T05:32:23.728523Z digest=sha256:77b73eb8aab9850d91dfc6e3516a6388a17682ed789af66724bcb6be49fc9aab

Observation d8e2e6c9-56e8-43a2-adc9-5c2c1e43bf81 · inbound

cMoLLM at Scale: Horizontal Scaling Laws for Mixture-of-LLMs cites this paper.

cMoLLM at Scale: Horizontal Scaling Laws for Mixture-of-LLMs DyNet: Dynamic Convolution for Accelerating Convolutional Neural Networks

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-02T12:12:35.715372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T12:12:35.715372Z digest=sha256:4c25c5695b00a858667f13b3df1d81cacc71017bf8f7e94168dc057ff0e1fe22