Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1902.04811.
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 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T12:47:24.843115Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T07:36:45.174590Z
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 1dd7419e-d602-486b-91bf-1021caae4a54 · inbound
Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate cites this paper.
Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T10:25:38.607286Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2f123d49-9a9a-4cf4-9cc6-d8625eafd6d8 · inbound
Distributed Learning in Non-Convex Environments -- Part II: Polynomial Escape from Saddle-Points cites this paper.
Distributed Learning in Non-Convex Environments -- Part II: Polynomial Escape from Saddle-Points On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T09:55:36.558456Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 0ba4b784-de7f-4123-84e1-5d03894fab8a · inbound
Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning cites this paper.
Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:47:24.843115Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 225efb58-aadc-4cae-98ad-130710dfabd3 · inbound
Second-Order Guarantees of Stochastic Gradient Descent in Non-Convex Optimization cites this paper.
Second-Order Guarantees of Stochastic Gradient Descent in Non-Convex Optimization On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:38:57.556595Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 28ebbe59-f608-4d49-b083-eabef028782a · inbound
Learning from Limited and Imperfect Data cites this paper.
Learning from Limited and Imperfect Data On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Reference 123
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T13:09:59.968238Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 251df33f-3283-40a0-903c-faf888aaccea · inbound
Globally aware optimization with resurgence cites this paper.
Globally aware optimization with resurgence On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T12:46:30.602557Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 824e1e3a-0fd3-407f-acec-4679c98c48a6 · inbound
Convergence of difference inclusions: a diameter criterion and step-size conditions cites this paper.
Convergence of difference inclusions: a diameter criterion and step-size conditions On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Reference 291
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T02:23:31.781196Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c25847ea-ec12-4c43-ab3d-d04b28236c62 · inbound
Sharp First-Order Lower Bounds for Higher-Order Smooth Nonconvex Optimization cites this paper.
Sharp First-Order Lower Bounds for Higher-Order Smooth Nonconvex Optimization On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T07:36:45.176067Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c5d90958-7f30-458b-9759-e8ee34f882d9 · inbound
Stochastic Saddle Avoidance Beyond Unit Excitation and Smoothness: A Pathwise Lyapunov-Perron Framework cites this paper.
Stochastic Saddle Avoidance Beyond Unit Excitation and Smoothness: A Pathwise Lyapunov-Perron Framework On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T04:30:36.947932Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.