Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Gradient Descent Converges to Minimizers
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1602.04915.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T00:02:21.159893Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-25T15:15:58.513087Z
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 29159f45-5a9e-4b4a-96df-ea590914c33b · inbound
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization cites this paper.
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization Gradient Descent Converges to Minimizers
Reference 23
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-25T15:15:58.518278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 9df7479d-8e4a-491f-abc6-2fe3cfd7fddb · inbound
Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape cites this paper.
Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape Gradient Descent Converges to Minimizers
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T02:15:14.271571Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation fa442d7b-3a21-4f11-aabf-ac5009e102ce · inbound
Accelerated Gradient Methods for Nonconvex Optimization: Escape Trajectories From Strict Saddle Points and Convergence to Local Minima cites this paper.
Accelerated Gradient Methods for Nonconvex Optimization: Escape Trajectories From Strict Saddle Points and Convergence to Local Minima Gradient Descent Converges to Minimizers
Reference 49
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-24T07:54:09.110612Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e5160cc1-301c-4771-a115-9aac20e4a3af · inbound
Landscape analysis for shallow neural networks: Complete classification of critical points for cubic activation and affine target functions cites this paper.
Landscape analysis for shallow neural networks: Complete classification of critical points for cubic activation and affine target functions Gradient Descent Converges to Minimizers
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T00:02:21.159893Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.