Paper Citation Record · LEDGER

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling

As of 13 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2411.17567.

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

pith.paper-citation-record.v1
2411.17567 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:08:32.250336Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T16:32:17.167850Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T01:27:31.159532Z

Reference resolution

38 of 38 outbound references displayed

verified exact1
verified fuzzy27
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 00f2e110-a43c-4e16-adf8-9db959313509 · outbound

This paper cites Abramowitz and I.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Abramowitz and I

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:35.353431Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e6de8100-8ec0-4e64-bb48-05c5890e0598 · outbound

This paper cites Towards diffusion approximations for stochastic gradient descent without replacement.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Towards diffusion approximations for stochastic gradient descent without replacement

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:35.276846Z

Source-reported events for the cited work

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Observation 816b1d7b-9536-4f6c-ae13-20ce12f56c30 · outbound

This paper cites an unresolved cited work.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Unresolved cited work

Reference 3

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Observation c20a1bee-8cce-48cf-a6ea-c3c4a4c1187f · outbound

This paper cites Gradients without Backpropagation.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Gradients without Backpropagation

Reference 4

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Source-reported events for the cited work

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Observation 5b6e24fc-69e5-4112-9710-d53cf6ad1fed · outbound

This paper cites Curriculumlearning.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Curriculumlearning

Reference 5

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Observation 25c7ca7e-9e6f-4448-a68e-e79e899c95a9 · outbound

This paper cites Learningsingle-indexmodelswithshallow neural networks.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Learningsingle-indexmodelswithshallow neural networks

Reference 6

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Source-reported events for the cited work

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Observation caa22045-2ec4-4b7d-9fd4-f3a8b467392a · outbound

This paper cites Convergence guarantees for forward gradient descent in the linear regression model.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Convergence guarantees for forward gradient descent in the linear regression model

Reference 7

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Observation ac6adee0-968d-4ee5-b755-693cc52a23a0 · outbound

This paper cites Is Learning in Biological Neural Networks Based on StochasticGradientDescent?AnAnalysisUsingStochasticProcesses.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Is Learning in Biological Neural Networks Based on StochasticGradientDescent?AnAnalysisUsingStochasticProcesses

Reference 8

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Observation 006a37bd-949d-4fcc-8a3c-7397459ac67f · outbound

This paper cites DropoutRegularizationVersusl2-Penalization in the Linear Model.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling DropoutRegularizationVersusl2-Penalization in the Linear Model

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:34.735847Z

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Observation e1f024f3-5089-413b-88d4-c8d7ab7f66ed · outbound

This paper cites an unresolved cited work.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Unresolved cited work

Reference 10

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Source-reported events for the cited work

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Observation e9749480-3018-49c5-ad96-82efef19e8db · outbound

This paper cites The recent excitement about neural networks.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling The recent excitement about neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:34.633210Z

Source-reported events for the cited work

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Observation 9eb34da9-8bac-41d3-a280-0e018c80851a · outbound

This paper cites Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:34.540098Z

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Observation 75c6f764-d711-408a-8104-46aa9cc02157 · outbound

This paper cites Optimal Rates for Zero- Order Convex Optimization: The Power of Two Function Evaluations.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Optimal Rates for Zero- Order Convex Optimization: The Power of Two Function Evaluations

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:34.506848Z

Source-reported events for the cited work

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Observation 0c4def28-2b34-4135-a017-084fc33a1003 · outbound

This paper cites LearningSingle-IndexModelsinGaussianSpace.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling LearningSingle-IndexModelsinGaussianSpace

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:34.320080Z

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Observation 418231ea-8506-4e9d-85c6-af3f0eb6ea4c · outbound

This paper cites Beyond the Regret Minimization Barrier: Optimal Algorithms for Stochastic Strongly-Convex Optimization.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Beyond the Regret Minimization Barrier: Optimal Algorithms for Stochastic Strongly-Convex Optimization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:34.298396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3f8fd844-2e88-4a60-be8d-3191bb8cd4d9 · outbound

This paper cites The organization of behavior: A neuropsychological theory.NewYork:Wiley,June.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling The organization of behavior: A neuropsychological theory.NewYork:Wiley,June

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:34.280724Z

Source-reported events for the cited work

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Observation 68817df9-d434-4626-a372-1c5a25cf6c74 · outbound

This paper cites Concentrationinequalitiesandmomentboundsforsam- plecovarianceoperators.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Concentrationinequalitiesandmomentboundsforsam- plecovarianceoperators

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:34.193489Z

Source-reported events for the cited work

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Observation d5f299b4-cfd9-48f4-be49-93cf771f32a7 · outbound

This paper cites Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit

Reference 18

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Source-reported events for the cited work

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Observation e8d487e4-8b77-487d-95f7-5d779e1f1a1d · outbound

This paper cites Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models

Reference 19

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Observation 09ebcf7f-dc95-4829-beb0-7ca32b2b920b · outbound

This paper cites Backpropagation and the brain.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Backpropagation and the brain

Reference 20

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Source-reported events for the cited work

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Observation c2223dfc-50a3-4eb9-9da5-bf5a5f37a252 · outbound

This paper cites A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:34.060295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c8fd3079-3fc1-485e-958e-5e1ebb56b6f9 · outbound

This paper cites Continuous-time limit of stochastic gradient descent revisited.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Continuous-time limit of stochastic gradient descent revisited

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1e0ef21f-32e0-411e-bdf7-e3a0be71f98d · outbound

This paper cites SGD without Replacement: Sharper Rates for General Smooth Convex Functions.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling SGD without Replacement: Sharper Rates for General Smooth Convex Functions

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:33.854818Z

Source-reported events for the cited work

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Observation ba164d68-6d08-412b-8325-42ff7b80ddb6 · outbound

This paper cites Random Gradient-Free Minimization of Convex Func- tions.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Random Gradient-Free Minimization of Convex Func- tions

Reference 24

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Observation 0582c209-10f4-4f95-bc31-ba78b7153730 · outbound

This paper cites Scaling Forward Gradient With Local Losses.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Scaling Forward Gradient With Local Losses

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:33.836576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 77e2ce15-d98c-4ae2-8e96-199fec3c3c47 · outbound

This paper cites Interpreting learning in biological neural networks as zero-order optimization method.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Interpreting learning in biological neural networks as zero-order optimization method

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-12T12:08:32.451945Z

Source-reported events for the cited work

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Observation 515dbff5-63d8-4ba9-a966-f7f5b20e9308 · outbound

This paper cites Hebbian learning inspired estimation of the linear regression parameters from queries.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Hebbian learning inspired estimation of the linear regression parameters from queries

Reference 27

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Source-reported events for the cited work

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Observation 2d8d38b9-9842-4493-a3fc-1ceffaf82704 · outbound

This paper cites Sgd: The role of implicit regularization, batch- sizeandmultiple-epochs.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Sgd: The role of implicit regularization, batch- sizeandmultiple-epochs

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:33.661997Z

Source-reported events for the cited work

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Observation e68bd503-0c2b-436f-8740-4cd8fc4dce89 · outbound

This paper cites Learningrelusviagradientdescent.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Learningrelusviagradientdescent

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:33.644587Z

Source-reported events for the cited work

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Observation 8ba97733-2e2f-4861-8c36-f4516b4dd019 · outbound

This paper cites Curriculum learning: A survey.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Curriculum learning: A survey

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:33.569973Z

Source-reported events for the cited work

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Observation 65019ef6-76c5-4b59-92d2-b8fb42676bac · outbound

This paper cites Deeplearn- inginspikingneuralnetworks.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Deeplearn- inginspikingneuralnetworks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:33.462209Z

Source-reported events for the cited work

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Observation 0a9d7877-5b61-4dbb-b4eb-834da75733b7 · outbound

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Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Unresolved cited work

Reference 32

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Source-reported events for the cited work

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Observation 02ceae06-2523-476c-9e72-14d443d7fa6c · outbound

This paper cites High-Dimensional Probability: An Introduction with Applications in Data Science.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling High-Dimensional Probability: An Introduction with Applications in Data Science

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:33.257371Z

Source-reported events for the cited work

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Observation 96778c89-4dbc-49f4-9288-97bdfd9b325e · outbound

This paper cites Theory of Curriculum Learning, with Convex Loss Func- tions.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Theory of Curriculum Learning, with Convex Loss Func- tions

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:33.138189Z

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Observation 7f3bfc5e-e51e-4c35-bc85-3017590c0a40 · outbound

This paper cites CurriculumLearningbyTransferLearning:Theory and Experiments with Deep Networks.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling CurriculumLearningbyTransferLearning:Theory and Experiments with Deep Networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:33.022804Z

Source-reported events for the cited work

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Observation 69096c47-285c-4dbc-a2cc-c4eaae233f6f · outbound

This paper cites Theories of Error Back-Propagation in the Brain.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Theories of Error Back-Propagation in the Brain

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:32.932013Z

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Observation 544f2893-97d5-410b-b8ec-8cf51a6bf919 · outbound

This paper cites On the statistical benefits of curriculum learning.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling On the statistical benefits of curriculum learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:32.836458Z

Source-reported events for the cited work

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Observation 9bf47b03-e550-4b92-802e-46983db17945 · outbound

This paper cites Optimal epoch stochastic gradient descent ascent methods for min-max optimization.

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling Optimal epoch stochastic gradient descent ascent methods for min-max optimization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:08:32.767976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation 69d85e0b-864b-4d75-a3f7-2c31dc94d7e5 · inbound

Adaptive directional gradients for parameterised quantum circuits cites this paper.

Adaptive directional gradients for parameterised quantum circuits Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:27:31.161043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.