Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2110.06897.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T14:02:56.695388Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-08T08:54:48.820886Z
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 d742f177-91c1-4d92-8f10-1ce7ffb62676 · inbound
Unifying Learning Dynamics and Generalization in Transformers Scaling Law cites this paper.
Unifying Learning Dynamics and Generalization in Transformers Scaling Law Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T14:02:56.695388Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 144b2457-b578-4186-9373-6790c9178cdf · inbound
Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs cites this paper.
Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:06:28.639356Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 52e38138-a066-4e1a-942e-59a1e76c9c2a · inbound
Concentration Inequalities for Sample Cross-Covariances cites this paper.
Concentration Inequalities for Sample Cross-Covariances Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
Reference 295
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T20:27:48.955192Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8fe8e3ce-7509-4d1a-a9ca-202fad28ab0b · inbound
Feature Learning for the High Dimensional Stationary Sch\"odinger Equation with Deep Ritz Method cites this paper.
Feature Learning for the High Dimensional Stationary Sch\"odinger Equation with Deep Ritz Method Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
Reference 33
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-08T08:54:48.822967Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 728abed7-90df-4a3e-81d3-1e02294adef1 · inbound
Elliptic Regularity Theory in Barron Spaces and Applications to the Deep Ritz Method cites this paper.
Elliptic Regularity Theory in Barron Spaces and Applications to the Deep Ritz Method Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T01:28:50.918033Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.