Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
The Computational Complexity of Training ReLU(s)
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1810.04207.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T16:59:44.125553Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-20T23:03:50.779885Z
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 0f722a24-4892-434d-8af8-d68959352d27 · inbound
Omnipredicting Single-Index Models with Multi-Index Models cites this paper.
Omnipredicting Single-Index Models with Multi-Index Models The Computational Complexity of Training ReLU(s)
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:59:44.125553Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93f7ac93-c078-4039-9b2d-7f7f668f1a3b · inbound
Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift cites this paper.
Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift The Computational Complexity of Training ReLU(s)
Reference 195
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T04:50:56.891924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 600542e2-911c-4d1b-9eeb-7f055f21b985 · inbound
Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift cites this paper.
Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift The Computational Complexity of Training ReLU(s)
Reference 195
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-20T23:03:50.781772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.