Paper Citation Record · LEDGER

Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2307.11782.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2307.11782 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:04:09.706749Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T23:57:27.990609Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 91f76ef9-c471-4125-a0ad-d1d96311a53c · inbound

Anisotropic Gaussian Smoothing for Gradient-based Optimization cites this paper.

Anisotropic Gaussian Smoothing for Gradient-based Optimization Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T18:23:49.145676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 449966e4-f16d-4285-a2b5-8a5db4a37bcd · inbound

Sharp higher order convergence rates for the Adam optimizer cites this paper.

Sharp higher order convergence rates for the Adam optimizer Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T06:04:09.706749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c6d270c-be68-45b0-b164-53450e6e1a82 · inbound

AdamS: Momentum Itself Can Be A Normalizer for LLM Pretraining and Post-training cites this paper.

AdamS: Momentum Itself Can Be A Normalizer for LLM Pretraining and Post-training Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:12.575287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 496e0674-4c33-4819-873e-f38e87aeb69b · inbound

On the Convergence of Muon and Beyond cites this paper.

On the Convergence of Muon and Beyond Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:56:34.188997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e4ddd552-3d89-4f1d-b629-137e6c37118d · inbound

OptMuon: Closed-Loop Orthogonalized Momentum Methods for Stochastic Optimization with Zero-Noise Optimality cites this paper.

OptMuon: Closed-Loop Orthogonalized Momentum Methods for Stochastic Optimization with Zero-Noise Optimality Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case

Reference 78

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:57:27.992159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 83c24c68-9d31-47ef-ab02-ff1d359d4421 · inbound

OptMuon: Closed-Loop Orthogonalized Momentum Methods for Stochastic Optimization with Zero-Noise Optimality cites this paper.

OptMuon: Closed-Loop Orthogonalized Momentum Methods for Stochastic Optimization with Zero-Noise Optimality Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case

Reference 84

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T05:43:08.848247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation edc81e3d-4ecf-4032-bf68-180408aa39d8 · 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 Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-11T20:46:05.467029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.