Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Representing smooth functions as compositions of near-identity functions with implications for deep network optimization
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1804.05012.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:30:50.613874Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-09T20:36:31.494955Z
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 531c7aa7-d33d-47f8-b998-66c7fda3912a · inbound
Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Nets cites this paper.
Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Nets Representing smooth functions as compositions of near-identity functions with implications for deep network optimization
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T15:45:59.281374Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation ba2ca55e-7f37-40e4-b6de-125b10bb953b · inbound
Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks cites this paper.
Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Representing smooth functions as compositions of near-identity functions with implications for deep network optimization
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:30:50.613874Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8e953837-908b-40ee-821e-c5dc1904f095 · inbound
Smooth Model Compression without Fine-Tuning cites this paper.
Smooth Model Compression without Fine-Tuning Representing smooth functions as compositions of near-identity functions with implications for deep network optimization
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:30:06.566686Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a967dee9-bd00-498b-bf84-9509ca13cf5f · inbound
Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsampling cites this paper.
Prior-aware and Context-guided Group Sampling for Active Probabilistic Subsampling Representing smooth functions as compositions of near-identity functions with implications for deep network optimization
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T20:36:31.496648Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.