Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2404.01436.
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 2026-08-09T17:00:29.467654Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T20:16:50.129635Z
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 537537c3-3ada-4c6c-99bd-86d2ec285909 · inbound
Refining Adaptive Zeroth-Order Optimization at Ease cites this paper.
Refining Adaptive Zeroth-Order Optimization at Ease Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T17:00:29.467654Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 64c45bb8-6591-43d5-a03d-7bd07c14bd4e · inbound
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness cites this paper.
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance
Reference 77
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:35:29.792126Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 62d9db7f-7614-4cbd-9382-b7706f10294e · inbound
Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism cites this paper.
Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:55:15.284168Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 604029ae-9eb3-43ec-9297-810eec074589 · inbound
Stochastic versus Deterministic in Stochastic Gradient Descent cites this paper.
Stochastic versus Deterministic in Stochastic Gradient Descent Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T20:16:50.133264Z
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.