Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1702.03849.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-30T22:19:23.705339Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-01T14:05:46.498138Z
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 cdc47fcf-6f36-43eb-9202-2f929d2881b1 · 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 Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T15:45:59.328867Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 36b8c910-5868-435f-912a-66ab842d4859 · inbound
SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems cites this paper.
SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T00:05:06.643350Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 74e0e55b-5266-44aa-9a52-00914ee8e115 · inbound
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data cites this paper.
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
Reference 4
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-01T14:05:46.499582Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.