Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2203.01664.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:46:20.742567Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T00:24:47.104168Z
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 d12edb14-3df5-4ca4-b5af-a318f10d7ec0 · inbound
Beyond the Norm: A Survey of Synthetic Data Generation for Rare Events Tail-GAN: Learning to Simulate Tail Risk Scenarios
Reference 12
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6ceb2de3-cd80-4606-be5c-12f1a5fa5806 · inbound
Dynamic data generation and dynamic portfolio selection: an application of a score-based diffusion model Tail-GAN: Learning to Simulate Tail Risk Scenarios
Reference 19
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2cf72a56-0161-48e1-81d7-4599911ca5e0 · inbound
Cold-Start Forecasting of New Product Life-Cycles via Conditional Diffusion Models Tail-GAN: Learning to Simulate Tail Risk Scenarios
Reference 39
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2f8e760f-b970-4548-9245-9601acfc9133 · inbound
An Extreme Value Perspective on Learning Stress Laws Tail-GAN: Learning to Simulate Tail Risk Scenarios
Reference 17
Source-reported events for the cited work
Unavailable: canonical work link unavailable.