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Paper Citation Record · LEDGER

Oblivious Sketching of High-Degree Polynomial Kernels

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1909.01410.

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

pith.paper-citation-record.v1
1909.01410 v5

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 2026-07-14T23:11:18.736508Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T04:15:20.196186Z

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 ec85eb10-3e62-40dc-9e7b-58befc4da745 · inbound

Linear-Scaling Tensor Train Sketching cites this paper.

Linear-Scaling Tensor Train Sketching Oblivious Sketching of High-Degree Polynomial Kernels

Reference 1

Resolution
unresolved
no resolver link, observed 2026-07-14T23:11:18.736508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T23:11:18.736508Z digest=sha256:cf360f0eaae7c420d043b3e129d333db5d950fc4ac5afb48c33fa5b3ec50e958

Observation 07d8e5b8-ca51-41ab-a056-60c823acb64a · inbound

Kernel-Based ReLU Approximation for Homomorphic Encryption-Compatible Privacy-preserving Deep Learning Models cites this paper.

Kernel-Based ReLU Approximation for Homomorphic Encryption-Compatible Privacy-preserving Deep Learning Models Oblivious Sketching of High-Degree Polynomial Kernels

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-25T04:15:20.199197Z

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=pdf_text observed=2026-05-25T04:11:56.046865Z digest=sha256:7a80668fdf0a7f2533ee125313d524d8612f5bc85efa2c9daee1677890eb4b62