Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.14340.
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 2026-08-16T06:04:09.665987Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-16T06:04:10.055415Z
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 30dfd595-5127-4ed5-8461-c3e428b208bc · inbound
Sharp higher order convergence rates for the Adam optimizer cites this paper.
Sharp higher order convergence rates for the Adam optimizer Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-08-16T06:04:10.062227Z
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 78ea1132-190c-4750-a503-54125cc817c9 · inbound
Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks cites this paper.
Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T20:46:05.467029Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.