Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1909.05136.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T00:02:21.277553Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T16:06:15.549683Z
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 875b3937-c3ec-41a0-9a49-2a672792a919 · inbound
ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations cites this paper.
ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T16:06:15.552926Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2a0c27ad-8814-4125-94fa-4375b7243280 · inbound
Landscape analysis for shallow neural networks: Complete classification of critical points for cubic activation and affine target functions cites this paper.
Landscape analysis for shallow neural networks: Complete classification of critical points for cubic activation and affine target functions PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T00:02:21.277553Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.