Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2302.04702.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:04:54.429777Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T14:49:33.430598Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation 34dbc9b1-411a-4bbd-80ab-095735b3d003 · inbound
CleanPatrick: A Benchmark for Image Data Cleaning REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines
Reference 7
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 375bf85c-60aa-4101-84e4-d755a8aafc37 · inbound
Stress-Testing ML Pipelines with Adversarial Data Corruption REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines
Reference 3
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b5ba39e5-44b1-42d5-94df-fe870296f20b · inbound
A Comparative Analysis of Influence Signals for Data Debugging REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines
Reference 1
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a00b0126-4fe1-4260-b94b-b33f82645d88 · inbound
Understanding and evaluating computer vision models through the lens of counterfactuals REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines
Reference 67
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.