Paper Citation Record · LEDGER

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms

As of 14 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2508.21022.

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

pith.paper-citation-record.v1
2508.21022 v3

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:49:00.506761Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:56:16.182901Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T23:40:53.124754Z

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation de6316a1-6906-48df-81e1-8f28f0e20209 · outbound

This paper cites Convergence of variational M onte C arlo simulation and scale-invariant pre-training.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Convergence of variational M onte C arlo simulation and scale-invariant pre-training

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:49:01.884953Z

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Observation daa8b3e1-376d-4e6d-8d50-73113679757b · outbound

This paper cites Natural gradient works efficiently in learning.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Natural gradient works efficiently in learning

Reference 2

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Observation 2cd01106-a8b5-41d1-89ca-b4ee030acf28 · outbound

This paper cites Functional Neural Wavefunction Optimization.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Functional Neural Wavefunction Optimization

Reference 3

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Observation be8ecc07-a232-44eb-a0ac-e7ad0baa68b4 · outbound

This paper cites Stochastic (approximate) proximal point methods: Convergence, optimality, and adaptivity.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Stochastic (approximate) proximal point methods: Convergence, optimality, and adaptivity

Reference 4

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Observation 73a66fba-6894-40ef-9865-ab1719645d1f · outbound

This paper cites Incremental proximal methods for large scale convex optimization.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Incremental proximal methods for large scale convex optimization

Reference 5

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Observation 742a798d-7be0-41c9-a1ff-5a643cac1ada · outbound

This paper cites Exact and inexact subsampled N ewton methods for optimization.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Exact and inexact subsampled N ewton methods for optimization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:49:01.757853Z

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Observation ccd96d14-452f-4294-81a9-de794976b1f9 · outbound

This paper cites Optimization methods for large-scale machine learning.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Optimization methods for large-scale machine learning

Reference 7

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Observation 1ad99cec-ea44-402b-856b-1ac3ee9ab812 · outbound

This paper cites Learning the ground state of a non-stoquastic quantum H amiltonian in a rugged neural network landscape.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Learning the ground state of a non-stoquastic quantum H amiltonian in a rugged neural network landscape

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:49:01.714841Z

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Observation e1a7367f-3f7e-4917-ab79-c3e1861516b4 · outbound

This paper cites Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems

Reference 9

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Observation 76a1752b-9343-4130-bd23-f93fffe123d3 · outbound

This paper cites Solving the quantum many-body problem with artificial neural networks.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Solving the quantum many-body problem with artificial neural networks

Reference 10

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Observation e7b77d1c-4eea-4d33-99cc-dc35d0036c4e · outbound

This paper cites Empowering deep neural quantum states through efficient optimization.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Empowering deep neural quantum states through efficient optimization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:49:01.665829Z

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Observation c2cdfa77-2d01-4dd6-8c52-e194aac14364 · outbound

This paper cites Kronecker-factored approximate curvature for physics-informed neural networks.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Kronecker-factored approximate curvature for physics-informed neural networks

Reference 12

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Observation 3102763a-9f97-4ff0-8ac8-77d920f93fda · outbound

This paper cites Stochastic model-based minimization of weakly convex functions.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Stochastic model-based minimization of weakly convex functions

Reference 13

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Observation 3553b4b1-1c2e-4555-b491-ee2434bbd00c · outbound

This paper cites Sharp analysis of sketch-and-project methods via a connection to randomized singular value decomposition.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Sharp analysis of sketch-and-project methods via a connection to randomized singular value decomposition

Reference 14

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Observation eea4c31e-bf3e-42bc-9386-e7dde08c9776 · outbound

This paper cites Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems

Reference 15

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Observation 91546ce5-0fef-46f0-9642-ad475273b6c7 · outbound

This paper cites Randomized Kaczmarz Methods with Beyond-Krylov Convergence.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Randomized Kaczmarz Methods with Beyond-Krylov Convergence

Reference 16

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Observation 967713e3-67a4-49b8-9684-23ed1cbf2d3d · outbound

This paper cites Randomized Kaczmarz with tail averaging.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Randomized Kaczmarz with tail averaging

Reference 17

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Observation b4394677-3d40-4776-9022-79941a13b3bd · outbound

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

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Handbook of Convergence Theorems for (Stochastic) Gradient Methods

Reference 18

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Observation c3d9a9be-f8e2-40ff-ab1a-87e73c5bd4b2 · outbound

This paper cites A K aczmarz-inspired approach to accelerate the optimization of neural network wavefunctions.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms A K aczmarz-inspired approach to accelerate the optimization of neural network wavefunctions

Reference 19

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Observation 94aa17a4-d3e5-47ec-9621-149cf688b72b · outbound

This paper cites Worth their weight: Randomized and regularized block Kaczmarz algorithms without preprocessing.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Worth their weight: Randomized and regularized block Kaczmarz algorithms without preprocessing

Reference 20

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Observation 372524bc-7f3f-42a3-85b1-bb107e4836f0 · outbound

This paper cites Accelerated stochastic matrix inversion: general theory and speeding up BFGS rules for faster second-order optimization.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Accelerated stochastic matrix inversion: general theory and speeding up BFGS rules for faster second-order optimization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:49:01.547402Z

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Observation 18c8985c-fc75-429d-b7be-f6fb1617a07e · outbound

This paper cites Randomized iterative methods for linear systems.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Randomized iterative methods for linear systems

Reference 22

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Observation e8a55508-3746-4116-b34f-c3e8cef3ce84 · outbound

This paper cites Solving the Hubbard model with Neural Quantum States.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Solving the Hubbard model with Neural Quantum States

Reference 23

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Observation 565772f5-fb68-4656-b2d3-5db6955e5216 · outbound

This paper cites Improving energy natural gradient descent through woodbury, momentum, and randomization.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Improving energy natural gradient descent through woodbury, momentum, and randomization

Reference 24

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Observation 09c70ade-d093-45e3-8252-b54e63f4805d · outbound

This paper cites a tzle, and Frank No \'e . Deep-neural-network solution of the electronic S chr \.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms a tzle, and Frank No \'e . Deep-neural-network solution of the electronic S chr \

Reference 25

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Observation 0fb2fcf4-f2d0-47fd-a322-84bdac11e6b6 · outbound

This paper cites Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:49:01.484479Z

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Observation 18684f21-6038-405c-ad2d-aad3ce6d68b7 · outbound

This paper cites Neural Scaling Laws Surpass Chemical Accuracy for the Many-Electron Schr\"odinger Equation.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Neural Scaling Laws Surpass Chemical Accuracy for the Many-Electron Schr\"odinger Equation

Reference 27

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Observation d971a1a0-0bb5-42d2-81cd-1b2a64ca1dcb · outbound

This paper cites Characterizing possible failure modes in physics-informed neural networks.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Characterizing possible failure modes in physics-informed neural networks

Reference 28

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Observation 90835d8f-76f1-4a0e-ab07-27847a9556f3 · outbound

This paper cites Accelerated Natural Gradient Method for Parametric Manifold Optimization.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Accelerated Natural Gradient Method for Parametric Manifold Optimization

Reference 29

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Observation d1dd41a5-2a9a-4abc-b4cd-9cbed69baf50 · outbound

This paper cites Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators

Reference 30

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Observation c8b3f353-a3b8-408e-be0a-501ea885112a · outbound

This paper cites Ab initio calculation of real solids via neural network ansatz.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Ab initio calculation of real solids via neural network ansatz

Reference 31

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Observation e460a45b-dfd0-4749-93f9-b817ddae933f · outbound

This paper cites The power of interpolation: Understanding the effectiveness of sgd in modern over-parametrized learning.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms The power of interpolation: Understanding the effectiveness of sgd in modern over-parametrized learning

Reference 32

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Observation 2787dd6b-bc9c-404b-9c52-1902e26cc8c8 · outbound

This paper cites New insights and perspectives on the natural gradient method.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms New insights and perspectives on the natural gradient method

Reference 33

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Observation 1084da51-dfac-42ca-879b-ec64c6c4511c · outbound

This paper cites Fast and furious convergence: Stochastic second order methods under interpolation.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Fast and furious convergence: Stochastic second order methods under interpolation

Reference 34

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Observation 11a40424-44cb-49c1-9a50-46d37d4547a5 · outbound

This paper cites Achieving high accuracy with PINN s via energy natural gradient descent.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Achieving high accuracy with PINN s via energy natural gradient descent

Reference 35

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Observation 87396a7e-5fd8-4a28-87b0-a05fc9347dec · outbound

This paper cites Paved with good intentions: analysis of a randomized block K aczmarz method.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Paved with good intentions: analysis of a randomized block K aczmarz method

Reference 36

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Observation 426e60d3-9d3a-4449-8e16-e77c44507119 · outbound

This paper cites Stochastic gradient descent, weighted sampling, and the randomized K aczmarz algorithm.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Stochastic gradient descent, weighted sampling, and the randomized K aczmarz algorithm

Reference 37

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Observation f57e8ee5-2373-4a31-a83a-b45814c0e824 · outbound

This paper cites Geometry of learning neural quantum states.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Geometry of learning neural quantum states

Reference 38

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Observation 28315eb8-f954-4c5f-908d-71257e9826de · outbound

This paper cites Ab initio solution of the many-electron S chr \"o dinger equation with deep neural networks.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Ab initio solution of the many-electron S chr \"o dinger equation with deep neural networks

Reference 39

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Observation f53da1b9-4d28-4f73-a72a-51f78bba02a1 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 40

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Observation e48237f0-31b8-490d-97bc-4de55f4f073b · outbound

This paper cites Challenges in Training PINNs: A Loss Landscape Perspective.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Challenges in Training PINNs: A Loss Landscape Perspective

Reference 41

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This paper cites Efficient Subsampled Gauss-Newton and Natural Gradient Methods for Training Neural Networks.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Efficient Subsampled Gauss-Newton and Natural Gradient Methods for Training Neural Networks

Reference 42

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Observation 6d35c56f-1e4e-4b9a-8256-372edb294e88 · outbound

This paper cites A simple linear algebra identity to optimize large-scale neural network quantum states.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms A simple linear algebra identity to optimize large-scale neural network quantum states

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:49:01.257297Z

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Observation 8468e88f-24d4-4abf-9eba-4ae11b5cebc9 · outbound

This paper cites Stochastic reformulations of linear systems: algorithms and convergence theory.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Stochastic reformulations of linear systems: algorithms and convergence theory

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:49:01.239925Z

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Observation 0420a710-87d3-4d7d-910b-0e59bf2c194c · outbound

This paper cites Sub-Sampled Newton Methods I: Globally Convergent Algorithms.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Sub-Sampled Newton Methods I: Globally Convergent Algorithms

Reference 45

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Observation 3e33c8db-00e8-4270-8bcc-ea1052eafb6e · outbound

This paper cites Sub-Sampled Newton Methods II: Local Convergence Rates.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Sub-Sampled Newton Methods II: Local Convergence Rates

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-05T14:49:00.619748Z

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Observation 6e0e94f5-f6ae-4398-831e-ae48c65b87f1 · outbound

This paper cites Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems

Reference 47

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Observation a1785ba3-c2ab-416d-9d7d-814d1e8918ee · outbound

This paper cites Unified variational approach description of ground-state phases of the two-dimensional electron gas.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Unified variational approach description of ground-state phases of the two-dimensional electron gas

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:49:01.222409Z

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Observation 55c51297-8596-41aa-a290-e937b1e71e37 · outbound

This paper cites Generalized L anczos algorithm for variational quantum M onte C arlo.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Generalized L anczos algorithm for variational quantum M onte C arlo

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:49:01.203104Z

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Observation 9db7b86d-f13a-45df-87e3-d69b26ce9361 · outbound

This paper cites When and why PINN s fail to train: A neural tangent kernel perspective.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms When and why PINN s fail to train: A neural tangent kernel perspective

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T14:49:00.457966Z

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Observation f99e645f-09c7-4f8b-aff4-0899a4c22c8e · outbound

This paper cites Rayleigh- G auss- N ewton optimization with enhanced sampling for variational M onte C arlo.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Rayleigh- G auss- N ewton optimization with enhanced sampling for variational M onte C arlo

Reference 51

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This paper cites Convergence analysis of an adaptively regularized natural gradient method.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Convergence analysis of an adaptively regularized natural gradient method

Reference 52

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This paper cites Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks

Reference 53

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This paper cites Sketchy Empirical Natural Gradient Methods for Deep Learning.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Sketchy Empirical Natural Gradient Methods for Deep Learning

Reference 54

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This paper cites Fast convergence of natural gradient descent for over-parameterized neural networks.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Fast convergence of natural gradient descent for over-parameterized neural networks

Reference 55

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This paper cites @esa (Ref.

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms @esa (Ref

Reference 56

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A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Unresolved cited work

Reference 57

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Reference 58

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Pith citing papers

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Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks cites this paper.

Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms

Reference 28

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