Paper Citation Record · LEDGER

Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2301.03580.

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

pith.paper-citation-record.v1
2301.03580 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:54:34.508875Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T03:06:30.208129Z

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 83feca3c-b794-4448-9591-5bb1c96a7167 · inbound

BiVM: Accurate Binarized Neural Network for Efficient Video Matting cites this paper.

BiVM: Accurate Binarized Neural Network for Efficient Video Matting Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T19:54:34.508875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1dc82df-c4b2-41d3-bbb5-4915770a3e1f · inbound

Advancing Metallic Surface Defect Detection via Anomaly-Guided Pretraining on a Large Industrial Dataset cites this paper.

Advancing Metallic Surface Defect Detection via Anomaly-Guided Pretraining on a Large Industrial Dataset Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T15:43:06.629544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d21bac3-e89f-441e-8ea5-dc3e67d74bce · inbound

BIAS: A Biologically Inspired Algorithm for Video Saliency Detection cites this paper.

BIAS: A Biologically Inspired Algorithm for Video Saliency Detection Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling

Reference 89

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:41:03.285355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0672aff3-4c1d-4eb0-8079-1a1b83f0bc91 · inbound

Structure-Guided Mixed Masked Pretraining and Spatial Continuity Regularization for Printed Circuit Board Defect Detection cites this paper.

Structure-Guided Mixed Masked Pretraining and Spatial Continuity Regularization for Printed Circuit Board Defect Detection Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:06:30.209799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.