Paper Citation Record · LEDGER

Why gradient clipping accelerates training: A theoretical justification for adaptivity

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 41 inbound Pith citation observations for arXiv:1905.11881.

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pith.paper-citation-record.v1
1905.11881 v2

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Pith citing papers itemized under the disclosed page cap.

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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

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HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization cites this paper.

HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 56

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Hidden Data Privacy Breaches in Federated Learning cites this paper.

Hidden Data Privacy Breaches in Federated Learning Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 54

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Toward a Unified Theory of Gradient Descent under Generalized Smoothness cites this paper.

Toward a Unified Theory of Gradient Descent under Generalized Smoothness Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 9

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Graph Spring Neural ODEs for Link Sign Prediction cites this paper.

Graph Spring Neural ODEs for Link Sign Prediction Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 11

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Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data cites this paper.

Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 101

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Hyperbolic Chamfer Distance for Point Cloud Completion and Beyond cites this paper.

Hyperbolic Chamfer Distance for Point Cloud Completion and Beyond Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 67

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Torque-Aware Momentum cites this paper.

Torque-Aware Momentum Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 46

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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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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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A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications and Challenges cites this paper.

A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications and Challenges Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 105

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Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs) cites this paper.

Gluon: Making Muon & Scion Great Again! (Bridging Theory and Practice of LMO-based Optimizers for LLMs) Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 30

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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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Gradient-Normalized Smoothness for Optimization with Approximate Hessians cites this paper.

Gradient-Normalized Smoothness for Optimization with Approximate Hessians Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 77

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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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Revisiting Randomized Smoothing: Nonsmooth Nonconvex Optimization Beyond Global Lipschitz Continuity cites this paper.

Revisiting Randomized Smoothing: Nonsmooth Nonconvex Optimization Beyond Global Lipschitz Continuity Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 43

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Observation 275d864c-e90d-4c78-b56f-a8aebcf0ad94 · inbound

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size cites this paper.

Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 95

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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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LiMuon: Light and Fast Muon Optimizer for Large Models cites this paper.

LiMuon: Light and Fast Muon Optimizer for Large Models Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 19

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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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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

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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

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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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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

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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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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

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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

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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

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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

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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

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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

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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

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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

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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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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

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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

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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

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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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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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Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization cites this paper.

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization Why gradient clipping accelerates training: A theoretical justification for adaptivity

Reference 295

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