Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2201.06699.
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 2026-08-15T20:56:01.555163Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T13:47:20.372810Z
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 058db889-b768-4322-9bf4-6baf69b74958 · inbound
A Training Framework for Optimal and Stable Training of Polynomial Neural Networks cites this paper.
A Training Framework for Optimal and Stable Training of Polynomial Neural Networks AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:56:01.555163Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3e8a53c1-52e3-4271-aa36-354dc85cf0bc · inbound
Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives cites this paper.
Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference
Reference 151
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:58:10.731096Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a91ffbf5-9d7b-4c33-b908-099e532ea7b7 · inbound
CryptoFace: End-to-End Encrypted Face Recognition cites this paper.
CryptoFace: End-to-End Encrypted Face Recognition AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference
Reference 39
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T13:47:20.378199Z
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 0d664510-f256-4f8c-9f61-1a69db03a4f0 · inbound
CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation cites this paper.
CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference
Reference 90
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T20:28:56.236480Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f741a7cc-450d-4305-93aa-e5d47dd86d25 · inbound
PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption cites this paper.
PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T17:39:40.298059Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.