Paper Citation Record · LEDGER

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness

As of 16 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2608.11479.

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pith.paper-citation-record.v1
2608.11479 v1

Coverage vector

measured 36 of 36 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-15T14:21:15.062219Z

measured 36 of 36 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation fe114f94-053f-4736-aab6-09c0fb164bb2 · outbound

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

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Why Do We Need Warm-up? A Theoretical Perspective

Reference 1

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This paper cites A Convergence Theory for Deep Learning via Over-Parameterization.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness A Convergence Theory for Deep Learning via Over-Parameterization

Reference 2

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This paper cites A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 3

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This paper cites On exact computation with an infinitely wide neural net.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness On exact computation with an infinitely wide neural net

Reference 4

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This paper cites Training Infinitely Deep and Wide Transformers.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Training Infinitely Deep and Wide Transformers

Reference 5

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Observation c57fdb88-ea85-4aec-9f8e-75bb0bf8b9fe · outbound

This paper cites A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications

Reference 6

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Observation 9441b31f-20dc-4881-823f-e408ba4dd5cc · outbound

This paper cites Non-Uniform Smoothness for Gradient Descent.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Non-Uniform Smoothness for Gradient Descent

Reference 7

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Observation 995d8129-df9f-4882-bdb2-941da3d24195 · outbound

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Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Unresolved cited work

Reference 8

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Observation 18269672-f597-4b1f-bb47-b544eb43c92a · outbound

This paper cites A Non-local Convergence Analysis of Gradient Flow for Deep Linear Networks.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness A Non-local Convergence Analysis of Gradient Flow for Deep Linear Networks

Reference 9

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Observation 9789eb47-7d0b-443e-9179-894606f7748b · outbound

This paper cites On lazy training in differentiable program- ming.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness On lazy training in differentiable program- ming

Reference 10

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Observation f41def0e-ee71-4d16-bb0b-9a0f557b1d91 · outbound

This paper cites Infinite-width limit of deep linear neural networks.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Infinite-width limit of deep linear neural networks

Reference 11

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Observation 8be0ee41-71cd-4e8e-bfc8-97959b2da425 · outbound

This paper cites Convergence of the Gradient Flow for Shallow ReLU Networks on Weakly Interacting Data.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Convergence of the Gradient Flow for Shallow ReLU Networks on Weakly Interacting Data

Reference 12

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Observation 69b4958e-7bdf-49b7-9c0c-36eb28aa3f1f · outbound

This paper cites Gradient Descent Provably Optimizes Over-parameterized Neural Networks.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Reference 13

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Observation 96703a10-e91c-4348-894f-96be9e9e9d9e · outbound

This paper cites Taming Nonconvex Stochastic Mirror Descent with General Breg- man Divergence.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Taming Nonconvex Stochastic Mirror Descent with General Breg- man Divergence

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:21:15.582638Z

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Observation 6cedc380-b8e6-47df-b0f7-12276fd291eb · outbound

This paper cites Glocal Smoothness: Line search and adaptive sizes can help in theory too!.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Glocal Smoothness: Line search and adaptive sizes can help in theory too!

Reference 15

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Observation 549c8ffc-c9b4-4b76-bac2-fbfa7ad3b166 · outbound

This paper cites Handbook of Convergence Theorems for (Stochastic) Gradient Methods.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Handbook of Convergence Theorems for (Stochastic) Gradient Methods

Reference 16

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Observation eb974be1-1875-4682-a936-b17e07888552 · outbound

This paper cites Neural tangent kernel: convergence and generalization in neural networks.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Neural tangent kernel: convergence and generalization in neural networks

Reference 17

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Observation eabb7d96-5134-4427-8990-940fd639dabf · outbound

This paper cites Convex and Non-convex Optimization Under Generalized Smoothness.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Convex and Non-convex Optimization Under Generalized Smoothness

Reference 18

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Observation ae41c33d-157f-4007-9c96-e6dd7f813e6d · outbound

This paper cites Learning overparameterized neural networks via stochastic gra- dient descent on structured data.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Learning overparameterized neural networks via stochastic gra- dient descent on structured data

Reference 19

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Observation e00e23e8-93a6-4866-8e3c-d31cb529a1d2 · outbound

This paper cites Relatively Smooth Convex Optimization by First-Order Methods, and Applications.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Relatively Smooth Convex Optimization by First-Order Methods, and Applications

Reference 20

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Observation 3c7d5104-320e-4f17-811b-6ca1abf734b2 · outbound

This paper cites Leveraging Non-uniformity in First-order Non-convex Optimization.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Leveraging Non-uniformity in First-order Non-convex Optimization

Reference 21

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Observation 541608ab-6407-4780-b848-35cfd4309719 · outbound

This paper cites Directional Smoothness and Gradient Methods: Convergence and Adaptiv- ity.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Directional Smoothness and Gradient Methods: Convergence and Adaptiv- ity

Reference 22

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Observation 227381fa-a489-476e-b6e0-e6ef42bd20ee · outbound

This paper cites On the Convergence Rate of LoRA Gradient Descent.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness On the Convergence Rate of LoRA Gradient Descent

Reference 23

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Observation 9c3fb1da-5182-44fc-8265-3bacd58946b1 · outbound

This paper cites On the Stability of Approximate Message Passing with Independent Measurement Ensembles.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness On the Stability of Approximate Message Passing with Independent Measurement Ensembles

Reference 24

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Observation 6755e76f-2d0b-404a-8fe5-51aa1dc66a6b · outbound

This paper cites Optimization for deep learning: theory and algorithms.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Optimization for deep learning: theory and algorithms

Reference 25

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Observation 8aec346c-9aae-41ab-b477-5613299f2a35 · outbound

This paper cites Fast Convergence in Learning Two-Layer Neural Networks with Separable Data.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Fast Convergence in Learning Two-Layer Neural Networks with Separable Data

Reference 26

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Observation ab03a82f-45f7-4cd2-b9c4-b0712dca37cc · outbound

This paper cites Convergence of Steepest Descent and Adam under Non-Uniform Smoothness.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Convergence of Steepest Descent and Adam under Non-Uniform Smoothness

Reference 27

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Observation e0e93428-a93e-450c-afb4-71f06a29f56c · outbound

This paper cites Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning

Reference 28

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Observation 5821e6c2-938d-4d3b-b76a-ff0663d7da99 · outbound

This paper cites Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions

Reference 29

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Observation 89f1a5ec-f06d-42c0-ab0b-2c49c1c520a0 · outbound

This paper cites Kernel and Rich Regimes in Overparametrized Models.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Kernel and Rich Regimes in Overparametrized Models

Reference 30

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Observation 4fe9efac-c325-4212-b0a1-689deb6440ba · outbound

This paper cites How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization

Reference 31

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Observation a25e1638-a39c-4fd3-920a-38e1e5fe263b · outbound

This paper cites Linear Convergence of Gradient Descent For Finite Width Over-parametrized Linear Networks With General Initialization.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Linear Convergence of Gradient Descent For Finite Width Over-parametrized Linear Networks With General Initialization

Reference 32

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Observation 33ec3a62-484d-42ea-a4e2-13147bd778c4 · outbound

This paper cites Feature Learning in Infinite-Width Neural Networks.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Feature Learning in Infinite-Width Neural Networks

Reference 33

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Observation 9adb5e76-ac83-4441-b2d8-87a6a7698834 · outbound

This paper cites Adaptive Gradient Normalization and Independent Sampling for (Stochas- tic) Generalized-Smooth Optimization.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Adaptive Gradient Normalization and Independent Sampling for (Stochas- tic) Generalized-Smooth Optimization

Reference 34

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Observation 15842e5a-c3f4-4b41-a283-9f86ee021cce · outbound

This paper cites On the Power and Limitations of Random Features for Un- derstanding Neural Networks.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness On the Power and Limitations of Random Features for Un- derstanding Neural Networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:21:15.448119Z

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Observation 598d1c27-64c0-4ef8-b3aa-4fccdefa9e46 · outbound

This paper cites Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity.

Convergence Guarantees of Gradient Descent for Neural Networks via Generalized Lipschitz Smoothness Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity

Reference 36

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