Paper Citation Record · LEDGER

Why gradient clipping accelerates training: A theoretical justification for adaptivity

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:1905.11881.

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

pith.paper-citation-record.v1
1905.11881 v2

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measured 27 of 27 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:53:06.399894Z

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

Source: arxiv_reference, observed 2026-07-04T10:29:44.281858Z

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Outbound references

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Pith citing papers

Observation a86adcb6-e681-4ab9-a614-da945dd611d2 · inbound

Adaptive Federated Optimization cites this paper.

Adaptive Federated Optimization Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-21T10:30:58.750778Z

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Observation 9efe2696-a607-4661-bf1a-dd5036aaca12 · inbound

The Ball-Proximal (="Broximal") Point Method: a New Algorithm, Convergence Theory, and Applications cites this paper.

The Ball-Proximal (="Broximal") Point Method: a New Algorithm, Convergence Theory, and Applications Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 80

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Observation 861108a2-3afd-4bbc-ba62-8ec0c4f76ebd · inbound

Generative Adversarial Networks Bridging Art and Machine Intelligence cites this paper.

Generative Adversarial Networks Bridging Art and Machine Intelligence Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 113

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Observation 06cc42a0-dda5-4f30-810e-c14ac27a2f86 · inbound

Revisiting Glorot Initialization for Long-Range Linear Recurrences cites this paper.

Revisiting Glorot Initialization for Long-Range Linear Recurrences Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 19

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Observation a6335d00-2cfa-4972-b4d5-e219fc3c4cc3 · inbound

Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness cites this paper.

Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 36

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Observation c0912f20-4171-4402-8969-4f2c1658cb0c · inbound

Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF cites this paper.

Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 27

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Observation b8c02199-f48d-4b06-a70f-52f3feac5a49 · inbound

Why Do We Need Warm-up? A Theoretical Perspective cites this paper.

Why Do We Need Warm-up? A Theoretical Perspective Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 73

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Observation 27440969-e32b-4d43-b5d9-71103666dfeb · inbound

Frank-Wolfe Algorithms for (L0, L1)-smooth functions cites this paper.

Frank-Wolfe Algorithms for (L0, L1)-smooth functions Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-18T06:20:58.638743Z

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Frank-Wolfe Algorithms for (L0, L1)-smooth functions cites this paper.

Frank-Wolfe Algorithms for (L0, L1)-smooth functions Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:20:34.576541Z

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Observation 20d74278-a8ba-4bf9-8261-862a7329ae4b · inbound

Non-Euclidean SGD for Structured Optimization: Unified Analysis and Improved Rates cites this paper.

Non-Euclidean SGD for Structured Optimization: Unified Analysis and Improved Rates Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 61

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Enroll-on-Wakeup: A First Comparative Study of Target Speech Extraction for Seamless Interaction in Real Noisy Human-Machine Dialogue Scenarios cites this paper.

Enroll-on-Wakeup: A First Comparative Study of Target Speech Extraction for Seamless Interaction in Real Noisy Human-Machine Dialogue Scenarios Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 36

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Observation 988fb1ce-3bbe-4d8b-b8c8-993a43c592ed · inbound

The Multi-Block DC Function Class: Theory, Algorithms, and Applications cites this paper.

The Multi-Block DC Function Class: Theory, Algorithms, and Applications Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:41:02.038729Z

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Observation 6e68920c-7d74-4ab5-84f4-755937775578 · inbound

Cost-Aware Learning cites this paper.

Cost-Aware Learning Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 22

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Observation f846a943-9d7a-4f89-8937-ca31226f7a17 · inbound

Distributionally Robust Multi-Objective Optimization cites this paper.

Distributionally Robust Multi-Objective Optimization Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:36:08.403171Z

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Observation d157392f-000c-49f2-accb-eaaba1b3f8c0 · inbound

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives cites this paper.

Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 71

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:37:19.224751Z

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Observation a0001604-ffe2-48eb-9c1c-dc1857fd795b · inbound

Newton methods beyond Hessian Lipschitz continuity: A nonlinear preconditioning approach cites this paper.

Newton methods beyond Hessian Lipschitz continuity: A nonlinear preconditioning approach Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:32:56.529199Z

Source-reported events for the cited work

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Observation e5fbf670-bc2a-4fb8-90d3-ad33f686465d · inbound

Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise cites this paper.

Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:12:38.850518Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1f146131-eb85-4a85-8fc0-e7849c9d0ac1 · inbound

Stochastic Non-Smooth Convex Optimization with Unbounded Gradients cites this paper.

Stochastic Non-Smooth Convex Optimization with Unbounded Gradients Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T15:17:39.256972Z

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Observation 708ae3b1-fc9e-4fcd-ad6e-d892f2e1a4ee · inbound

Revisiting Privacy Amplification by Subsampling in Selective Release DPSGD cites this paper.

Revisiting Privacy Amplification by Subsampling in Selective Release DPSGD Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:26:45.944148Z

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Observation 6e469f61-157a-4e59-8fdb-41d608a47084 · 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 Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:47:28.508872Z

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Observation 8a83ebba-1dcb-48fe-befc-68fd16b15394 · 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 Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-06-29T05:43:08.831493Z

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Observation 73ab61f4-3356-4830-8d1d-e42820f50ce0 · inbound

Convergence Analysis of Muon-type Methods with Inexact LMO in the Degenerate Case cites this paper.

Convergence Analysis of Muon-type Methods with Inexact LMO in the Degenerate Case Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 70

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Observation c557f51f-2960-4e93-ba02-ec489e64a96d · inbound

Distribution-Aware Robust Bilevel Optimization: Quantile-Guided Huber Updates in Two-Timescale Stochastic Approximation cites this paper.

Distribution-Aware Robust Bilevel Optimization: Quantile-Guided Huber Updates in Two-Timescale Stochastic Approximation Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:49:42.561321Z

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Observation b6e209a2-7cf9-497d-be8d-99afaaff0f66 · inbound

Convergence of Gradient Descent for General Neural Network Architectures Beyond the NTK Regime cites this paper.

Convergence of Gradient Descent for General Neural Network Architectures Beyond the NTK Regime Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:29:44.283468Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 68b1d868-e1bd-4e9d-9caf-4d5fa53315b6 · inbound

Normalized First-Order Methods for Convex (L0, L1)-Smooth Optimization with Inexact Gradients cites this paper.

Normalized First-Order Methods for Convex (L0, L1)-Smooth Optimization with Inexact Gradients Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 14

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Observation d7b0e0c6-7989-44c1-b181-901f25227c23 · inbound

The Convergence Behavior of Adam under Heavy-Tailed Noise cites this paper.

The Convergence Behavior of Adam under Heavy-Tailed Noise Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 2026

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The Convergence Behavior of Adam under Heavy-Tailed Noise cites this paper.

The Convergence Behavior of Adam under Heavy-Tailed Noise Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 2026

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