Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Data Noising as Smoothing in Neural Network Language Models
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1703.02573.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:41:17.021203Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-17T20:55:15.163500Z
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 565cfb40-5605-4f6a-af68-7a546b82ba8b · inbound
KAIROS: Scalable Model-Agnostic Data Valuation cites this paper.
KAIROS: Scalable Model-Agnostic Data Valuation Data Noising as Smoothing in Neural Network Language Models
Reference 64
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:41:17.021203Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 028036c6-cb3e-41b3-a2fb-61ad1d6d23ac · inbound
How Noise Benefits AI-generated Image Detection cites this paper.
How Noise Benefits AI-generated Image Detection Data Noising as Smoothing in Neural Network Language Models
Reference 70
- Resolution
- verified exact
- local_arxiv, observed 2026-05-17T20:55:15.165174Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 73cd72e4-066c-4aac-83cf-70f63d9d4502 · inbound
Pretraining Language Models with Subword Regularization: An Empirical Study of BPE Dropout in Low-Resource NLP cites this paper.
Pretraining Language Models with Subword Regularization: An Empirical Study of BPE Dropout in Low-Resource NLP Data Noising as Smoothing in Neural Network Language Models
Reference 31
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-14T19:32:52.672452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.