Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Efficiently learning and sampling multimodal distributions with data-based initialization
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2411.09117.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T15:51:16.006252Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T14:04:44.436505Z
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 94e273d1-0077-412a-9a40-8aa6dc8c4a0f · inbound
Better Models and Algorithms for Learning Ising Models from Dynamics cites this paper.
Better Models and Algorithms for Learning Ising Models from Dynamics Efficiently learning and sampling multimodal distributions with data-based initialization
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:51:16.006252Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c0236781-430c-4169-aa28-dcf35a383e08 · inbound
A Hybrid Framework for Healing Semigroups with Machine Learning cites this paper.
A Hybrid Framework for Healing Semigroups with Machine Learning Efficiently learning and sampling multimodal distributions with data-based initialization
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T12:18:00.281869Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77cd58f5-d4b7-48e7-be84-76d384190f27 · inbound
A computational phase transition for learning-to-sample from Ising models cites this paper.
A computational phase transition for learning-to-sample from Ising models Efficiently learning and sampling multimodal distributions with data-based initialization
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T14:04:44.438039Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.