Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Non-parametric regression for robot learning on manifolds
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2310.19561.
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 2026-08-11T17:27:56.388099Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T09:24:32.856696Z
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 2810565e-c7f7-4249-9b58-935ca39a981d · inbound
A cheat sheet for probability distributions of orientational data cites this paper.
A cheat sheet for probability distributions of orientational data Non-parametric regression for robot learning on manifolds
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T17:27:56.388099Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7d3df1b-cc78-41fd-b44e-489ace182479 · inbound
A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures cites this paper.
A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures Non-parametric regression for robot learning on manifolds
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:40:24.459313Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1009e82a-fe0c-4a90-b30b-ba9d41c91911 · inbound
Composition as Direction: An Active-Set Ray-Based Model for Sparse High-Dimensional Compositional Data cites this paper.
Composition as Direction: An Active-Set Ray-Based Model for Sparse High-Dimensional Compositional Data Non-parametric regression for robot learning on manifolds
Reference 154
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T09:24:32.858381Z
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.