Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.04592.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:45:07.761309Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-13T20:58:15.734279Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation d7452adf-7dba-458a-a72c-1b33a7ef44c3 · inbound
SGD with Adaptive Preconditioning: Unified Analysis and Momentum Acceleration Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization
Reference 18
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cb405e5a-dfea-42ed-b37a-658bd3181d3d · inbound
Non-Euclidean SGD for Structured Optimization: Unified Analysis and Improved Rates Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization
Reference 30
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1fbab019-480c-4942-9966-a31379e52df6 · inbound
Optimal Projection-Free Adaptive SGD for Matrix Optimization Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization
Reference 9
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 143d654f-2f15-4a05-99a1-b8169616974c · inbound
A unified convergence theory for adaptive first-order methods in the nonconvex case, including AdaNorm, full and diagonal AdaGrad, Shampoo and Muo Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization
Reference 27
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7ee54a71-d2e5-47f8-929b-96ac55cfdafa · inbound
Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization
Reference 9
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.