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Paper Citation Record · LEDGER

Exact Dynamics of Multi-class Stochastic Gradient Descent

As of 19 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2510.14074.

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

pith.paper-citation-record.v1
2510.14074 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:43:14.326705Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T17:31:02.850791Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T17:37:13.925711Z

Reference resolution

46 of 46 outbound references displayed

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Outbound references

Observation a07eb370-ec60-491e-956b-b025dfccb3b9 · outbound

This paper cites Escaping mediocrity: how two-layer networks learn hard single-index models with SGD.CoRR, 2023.

Exact Dynamics of Multi-class Stochastic Gradient Descent Escaping mediocrity: how two-layer networks learn hard single-index models with SGD.CoRR, 2023

Reference 1

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source=pdf_text observed=2026-08-04T09:43:08.662758Z digest=sha256:07e5340f3fb31fd19b96bd019d53f6c03f3efd1e044f41a14259485ca683c8ef

Observation 98e44c79-1ebd-4d77-b12c-2766efc8ece1 · outbound

This paper cites From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks.

Exact Dynamics of Multi-class Stochastic Gradient Descent From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks

Reference 2

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source=pdf_text observed=2026-08-04T09:43:08.759247Z digest=sha256:d39efc96919a2b92aacc81418d2fe6490b8b4a1259a9d75d1a0a7224049d0aa8

Observation 72a2f395-59a8-4157-8cf2-12836040937b · outbound

This paper cites Minimax theory for high-dimensional gaussian mixtures with sparse mean separation.Advances in Neural Information Processing Systems, 26, 2013.

Exact Dynamics of Multi-class Stochastic Gradient Descent Minimax theory for high-dimensional gaussian mixtures with sparse mean separation.Advances in Neural Information Processing Systems, 26, 2013

Reference 3

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source=pdf_text observed=2026-08-04T09:43:08.934177Z digest=sha256:36a4a003b5299f4a4ef3dfaa99221f60269c992d5d5bad0cf1c2faebcf6de675

Observation 18c8ed93-829a-479d-85a1-07b97bd8be7c · outbound

This paper cites High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance.

Exact Dynamics of Multi-class Stochastic Gradient Descent High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance

Reference 4

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source=pdf_text observed=2026-08-04T09:43:09.084449Z digest=sha256:7f7c10d2256b1b891256c14c18b067d42bb8e8e407ee74be9592f22d000b3780

Observation 0beadbd3-0bb5-49e5-9407-9e5cde07a855 · outbound

This paper cites Spectrally-normalized margin bounds for neural networks.Advances in neural information processing systems, 30, 2017.

Exact Dynamics of Multi-class Stochastic Gradient Descent Spectrally-normalized margin bounds for neural networks.Advances in neural information processing systems, 30, 2017

Reference 5

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source=pdf_text observed=2026-08-04T09:43:09.225286Z digest=sha256:f7cfcc0010e2f746c144314573d90d06f81c8ede8788dabe8ea71642f5b7dde2

Observation 2cdc4722-4599-4f3e-808f-a2b1b33ba480 · outbound

This paper cites Local geometry of high-dimensional mixture models: Effective spectral theory and dynamical transitions.arXiv preprint arXiv:2502.15655, 2025.

Exact Dynamics of Multi-class Stochastic Gradient Descent Local geometry of high-dimensional mixture models: Effective spectral theory and dynamical transitions.arXiv preprint arXiv:2502.15655, 2025

Reference 6

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source=pdf_text observed=2026-08-04T09:43:09.374734Z digest=sha256:d895ece22c152b6fbdb2ad29a5560e3e7e8141e542baeb20f404c83ce7c94265

Observation d458c512-1236-46e6-ab75-f3dbefd2298e · outbound

This paper cites Online stochastic gradient descent on non- convex losses from high-dimensional inference.The Journal of Machine Learning Research, 22(1):4788– 4838, 2021.

Exact Dynamics of Multi-class Stochastic Gradient Descent Online stochastic gradient descent on non- convex losses from high-dimensional inference.The Journal of Machine Learning Research, 22(1):4788– 4838, 2021

Reference 7

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source=pdf_text observed=2026-08-04T09:43:09.508327Z digest=sha256:b3d7779744791ba9e21bad525c7f0cfa0d3a6f31ee5c54ced230a6ac2997953d

Observation 7c4ee186-ed31-44c2-a7a0-65d68e331b73 · outbound

This paper cites High-dimensional limit theorems for SGD: Effective dynamics and critical scaling.

Exact Dynamics of Multi-class Stochastic Gradient Descent High-dimensional limit theorems for SGD: Effective dynamics and critical scaling

Reference 8

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source=pdf_text observed=2026-08-04T09:43:09.689739Z digest=sha256:ff08ae95c56cf8b562241f54733baa4cd4d803749b1b6b9a74585a6d1cdc372e

Observation 592d777b-d2b0-4249-a33a-0d0d9ac6677a · outbound

This paper cites On-line learning with a perceptron.Europhysics Letters, 28(7):525, 1994.

Exact Dynamics of Multi-class Stochastic Gradient Descent On-line learning with a perceptron.Europhysics Letters, 28(7):525, 1994

Reference 9

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source=pdf_text observed=2026-08-04T09:43:09.820533Z digest=sha256:67335e25aab1972abd5369f900cdebba0ff839a0c57104dd9071cea6847f4bf7

Observation ae175791-8f25-4d48-9804-25b7baae7430 · outbound

This paper cites Learning by on-line gradient descent.

Exact Dynamics of Multi-class Stochastic Gradient Descent Learning by on-line gradient descent

Reference 10

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Observation 911930c8-2506-470c-a881-4429791750c4 · outbound

This paper cites Learning curves for sgd on structured features.

Exact Dynamics of Multi-class Stochastic Gradient Descent Learning curves for sgd on structured features

Reference 11

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source=pdf_text observed=2026-08-04T09:43:10.059555Z digest=sha256:f1f1825d460703b0cb63668c74d5db2bc779c5b1c15a6e8ff0ddacaddf5abe03

Observation 0b35ee0d-7fa7-405b-b2e8-ec3fcdda8a1d · outbound

This paper cites The high-dimensional asymptotics of first order methods with random data.

Exact Dynamics of Multi-class Stochastic Gradient Descent The high-dimensional asymptotics of first order methods with random data

Reference 12

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source=pdf_text observed=2026-08-04T09:43:10.183758Z digest=sha256:45ee61de1f0af3fc8fa7496fe95fa2e91b38593d86d8e5687cfe31753b6836c7

Observation d7af9c46-eba3-4cfe-b74a-62b2e4f45cda · outbound

This paper cites Sharp global convergence guarantees for iterative nonconvex optimization with random data.Ann.

Exact Dynamics of Multi-class Stochastic Gradient Descent Sharp global convergence guarantees for iterative nonconvex optimization with random data.Ann

Reference 13

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source=pdf_text observed=2026-08-04T09:43:10.322964Z digest=sha256:e93221e743d7270bdaace984bf9b98589af79b7b716bf3c3ec67732d011e28f9

Observation 63db4703-0472-4232-87da-2088ce0fb78c · outbound

This paper cites Achieving optimal clustering in gaussian mixture models with anisotropic covariance structures.

Exact Dynamics of Multi-class Stochastic Gradient Descent Achieving optimal clustering in gaussian mixture models with anisotropic covariance structures

Reference 14

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source=pdf_text observed=2026-08-04T09:43:10.489704Z digest=sha256:12895543175dea37c634acd0accac4fa4ebf2c55ddff35b901ad08e9db449687

Observation 9733b769-f2e7-4184-965f-10bd638991bb · outbound

This paper cites Hitting the high- dimensional notes: An ode for sgd learning dynamics on glms and multi-index models.Information and Inference: A Journal of the IMA, 13(4):iaae028, 2024.

Exact Dynamics of Multi-class Stochastic Gradient Descent Hitting the high- dimensional notes: An ode for sgd learning dynamics on glms and multi-index models.Information and Inference: A Journal of the IMA, 13(4):iaae028, 2024

Reference 15

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source=pdf_text observed=2026-08-04T09:43:10.616862Z digest=sha256:de69f21bdfdee6ad37bb5ee1ccdfc9b378c3f144936939b7f62a03ca9c7f1a16

Observation 0d2a9513-7772-4d20-9b5c-1b463757b0af · outbound

This paper cites High-dimensional limit of one-pass SGD on least squares.

Exact Dynamics of Multi-class Stochastic Gradient Descent High-dimensional limit of one-pass SGD on least squares

Reference 16

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source=pdf_text observed=2026-08-04T09:43:10.795167Z digest=sha256:c959fe051f661cd37a88067e7a2f26ea8974d938834dacaee6e1a6e1701ffc13

Observation 57f81619-5ad7-4279-9022-3b437ceea752 · outbound

This paper cites Smoothing the landscape boosts the signal for sgd: Optimal sample complexity for learning single index models.Advances in Neural Information Processing Systems, 36:752–784, 2023.

Exact Dynamics of Multi-class Stochastic Gradient Descent Smoothing the landscape boosts the signal for sgd: Optimal sample complexity for learning single index models.Advances in Neural Information Processing Systems, 36:752–784, 2023

Reference 17

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source=pdf_text observed=2026-08-04T09:43:10.920521Z digest=sha256:4f93cd9f448a7c514f1dd4824c133ec9d3cdc2aeddae2d4b75e7f8d7b313c0c6

Observation ca8ada67-5ec5-4a6e-abcc-8087aa093570 · outbound

This paper cites Universality laws for gaussian mixtures in generalized linear models.Advances in Neural Information Processing Systems, 36, 2024.

Exact Dynamics of Multi-class Stochastic Gradient Descent Universality laws for gaussian mixtures in generalized linear models.Advances in Neural Information Processing Systems, 36, 2024

Reference 18

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source=pdf_text observed=2026-08-04T09:43:11.046787Z digest=sha256:071541495ce707f2443d29cfa12b236cd826ed1b25d76bafba982318d92b0ac4

Observation 79d8822b-e7d6-4a7b-963f-72e8ef0f720a · outbound

This paper cites The benefits of reusing batches for gradient descent in two-layer networks: Breaking the curse of information and leap exponents.

Exact Dynamics of Multi-class Stochastic Gradient Descent The benefits of reusing batches for gradient descent in two-layer networks: Breaking the curse of information and leap exponents

Reference 19

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source=pdf_text observed=2026-08-04T09:43:11.142071Z digest=sha256:4ae7000a7dfb12e8a8ffc63e06a8fa242244cc59aede6dcdc78d3b509390e4ac

Observation 3cac6827-63f8-4bac-9b6e-2246a7db3ada · outbound

This paper cites High-dimensional asymptotics of prediction: Ridge regression and classification.

Exact Dynamics of Multi-class Stochastic Gradient Descent High-dimensional asymptotics of prediction: Ridge regression and classification

Reference 20

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source=pdf_text observed=2026-08-04T09:43:11.291923Z digest=sha256:88651a9a88600b40559142b0b27e66e7d8ba5fe1b9ad98ba5afe7590fcd6fc9f

Observation 32f67fba-4131-4916-80df-010cb57c48fb · outbound

This paper cites Rigorous dynamical mean-field theory for stochastic gradient descent methods.

Exact Dynamics of Multi-class Stochastic Gradient Descent Rigorous dynamical mean-field theory for stochastic gradient descent methods

Reference 21

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source=pdf_text observed=2026-08-04T09:43:11.411374Z digest=sha256:aa59438bfa08e14aa1eab01d7ac8924f84e695798beb940748f8907c9bc9b222

Observation 06024813-f1f2-4a30-99df-92033055e1a7 · outbound

This paper cites Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup.Advances in neural information processing systems, 32, 2019.

Exact Dynamics of Multi-class Stochastic Gradient Descent Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup.Advances in neural information processing systems, 32, 2019

Reference 22

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source=pdf_text observed=2026-08-04T09:43:11.557818Z digest=sha256:b935ce8d36ec33d26eb864427db8e8121a0211ca4e136eaebb40131575e8b28a

Observation c914d719-70ce-4d17-98a2-000d0d772ebe · outbound

This paper cites The gaussian equivalence of generative models for learning with shallow neural networks.

Exact Dynamics of Multi-class Stochastic Gradient Descent The gaussian equivalence of generative models for learning with shallow neural networks

Reference 23

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source=pdf_text observed=2026-08-04T09:43:11.680596Z digest=sha256:dae6e6dc5debd6d97c60d069660224499908fa80033428579c7a3280ef1d893f

Observation ebe60c50-00fb-467d-97f1-a9c1a4b1fb3b · outbound

This paper cites Modeling the influence of data structure on learning in neural networks: The hidden manifold model.Physical Review X, 10(4):041044, 2020.

Exact Dynamics of Multi-class Stochastic Gradient Descent Modeling the influence of data structure on learning in neural networks: The hidden manifold model.Physical Review X, 10(4):041044, 2020

Reference 24

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source=pdf_text observed=2026-08-04T09:43:11.824766Z digest=sha256:b7e0c5efbc791f2e58118ed6e05d80232906a5f3ba94eaacbd3be2279791516b

Observation c8fafa65-3eec-46e0-80d4-2054cc3e4fa4 · outbound

This paper cites Minimax-optimal covariance projected spectral clustering for high- dimensional nonspherical mixtures.arXiv preprint arXiv:2502.02580, 2025.

Exact Dynamics of Multi-class Stochastic Gradient Descent Minimax-optimal covariance projected spectral clustering for high- dimensional nonspherical mixtures.arXiv preprint arXiv:2502.02580, 2025

Reference 25

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source=pdf_text observed=2026-08-04T09:43:12.001956Z digest=sha256:45f3aa34f97f68ecb8ddbb43a647807da8d544b119f207d498ad0441dfccb0a2

Observation d5dee034-428e-42b1-af0b-a078d524f833 · outbound

This paper cites Fast margin maximization via dual acceleration.

Exact Dynamics of Multi-class Stochastic Gradient Descent Fast margin maximization via dual acceleration

Reference 26

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source=pdf_text observed=2026-08-04T09:43:12.170682Z digest=sha256:c908eb7eda7f1d24e4161e434aab4056b312d6ad7960aa036e3537724168094b

Observation 23e8dfae-b4d7-48fe-913f-740e913dd6aa · outbound

This paper cites Characterizing the implicit bias via a primal-dual analysis.

Exact Dynamics of Multi-class Stochastic Gradient Descent Characterizing the implicit bias via a primal-dual analysis

Reference 27

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source=pdf_text observed=2026-08-04T09:43:12.350658Z digest=sha256:ac9fcccf2b9c7d64799a80388f1b12334a8a83edd9a5e54e396725b1be93b365

Observation 225d8dd0-1a8d-46ae-af0f-116d5d8e0ea7 · outbound

This paper cites Trajectory of mini-batch momentum: batch size saturation and convergence in high dimensions.Advances in Neural Information Processing Systems, 35:36944–36957, 2022.

Exact Dynamics of Multi-class Stochastic Gradient Descent Trajectory of mini-batch momentum: batch size saturation and convergence in high dimensions.Advances in Neural Information Processing Systems, 35:36944–36957, 2022

Reference 28

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Observation f6398622-2e8f-4bcd-90c5-c1fd43371e18 · outbound

This paper cites Phase transitions and optimal algorithms in high-dimensional gaussian mixture clustering.

Exact Dynamics of Multi-class Stochastic Gradient Descent Phase transitions and optimal algorithms in high-dimensional gaussian mixture clustering

Reference 29

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source=pdf_text observed=2026-08-04T09:43:12.607058Z digest=sha256:5242efac4404ab70876b718c1d8eb2dddb0df9eca42bfcc6f736c2d35c8d851e

Observation 7e71a636-828d-4233-b751-052319f99cae · outbound

This paper cites Optimality of spectral clustering in the gaussian mixture model.The Annals of Statistics, 49(5):2506–2530, 2021.

Exact Dynamics of Multi-class Stochastic Gradient Descent Optimality of spectral clustering in the gaussian mixture model.The Annals of Statistics, 49(5):2506–2530, 2021

Reference 30

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Observation edd76747-7d6b-432f-a13e-91284c25cfb3 · outbound

This paper cites Learning curves of generic features maps for realistic datasets with a teacher-student model.

Exact Dynamics of Multi-class Stochastic Gradient Descent Learning curves of generic features maps for realistic datasets with a teacher-student model

Reference 31

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source=pdf_text observed=2026-08-04T09:43:12.822188Z digest=sha256:bfa048edf428cf9210d87171f793b19110c5cb6390736efb53d4a38831d9e8e3

Observation 0b0923cf-3f23-402d-97f9-d8ee74acd3c6 · outbound

This paper cites Learning gaussian mixtures with generalized linear models: Precise asymptotics in high- dimensions.

Exact Dynamics of Multi-class Stochastic Gradient Descent Learning gaussian mixtures with generalized linear models: Precise asymptotics in high- dimensions

Reference 32

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source=pdf_text observed=2026-08-04T09:43:12.932987Z digest=sha256:7f6a4a1979b6e72e9b91383bc77b708412426960476b9fed554ad063950c9794

Observation 0ef4e9f0-f1d2-40cf-bfbf-b4f6e456a810 · outbound

This paper cites High dimensional classification via regularized and unregularized empirical risk minimization: Precise error and optimal loss.stat, 1050:25, 2020.

Exact Dynamics of Multi-class Stochastic Gradient Descent High dimensional classification via regularized and unregularized empirical risk minimization: Precise error and optimal loss.stat, 1050:25, 2020

Reference 33

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source=pdf_text observed=2026-08-04T09:43:13.108461Z digest=sha256:62fe8aa0ad14137c0935cc0410d2b7c30cf7a3035158039352754c6cd00be7db

Observation 94a02753-fd48-422c-b009-0b2c9279c068 · outbound

This paper cites Dynamical mean- field theory for stochastic gradient descent in gaussian mixture classification.

Exact Dynamics of Multi-class Stochastic Gradient Descent Dynamical mean- field theory for stochastic gradient descent in gaussian mixture classification

Reference 34

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source=pdf_text observed=2026-08-04T09:43:13.248060Z digest=sha256:8b1ab2181a4177607fe7fafe600433342e845c840bb0577c34eed1759b5a3cb8

Observation 5fc4034c-9c7f-46c7-a8a4-fc16225d5a0b · outbound

This paper cites Convergence of gradient descent on separable data.

Exact Dynamics of Multi-class Stochastic Gradient Descent Convergence of gradient descent on separable data

Reference 35

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source=pdf_text observed=2026-08-04T09:43:13.365838Z digest=sha256:6adb0b2ca86dff1d870c21257f834a5b85663d3ea358ae7f515c2076fc16c13d

Observation 94acbf43-863a-41c0-8ade-931efa361d56 · outbound

This paper cites The full spectrum of deepnet hessians at scale: Dynamics with SGD training and sample size.

Exact Dynamics of Multi-class Stochastic Gradient Descent The full spectrum of deepnet hessians at scale: Dynamics with SGD training and sample size

Reference 36

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Observation 5106c7c0-c783-4452-9c28-74467aafc06b · outbound

This paper cites Homogenization of SGD in high-dimensions: Exact dynamics and generalization properties.Mathematical Programming, pages 1–90, 2024.

Exact Dynamics of Multi-class Stochastic Gradient Descent Homogenization of SGD in high-dimensions: Exact dynamics and generalization properties.Mathematical Programming, pages 1–90, 2024

Reference 37

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Observation b3c8c4f7-e21b-4323-a5d8-9169e8675505 · outbound

This paper cites Classifying high-dimensional gaussian mixtures: Where kernel methods fail and neural networks succeed.

Exact Dynamics of Multi-class Stochastic Gradient Descent Classifying high-dimensional gaussian mixtures: Where kernel methods fail and neural networks succeed

Reference 38

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Observation 6f17a1bd-6c00-4098-bbba-d05b57cd5784 · outbound

This paper cites Dynamics of on-line gradient descent learning for multilayer neural networks.

Exact Dynamics of Multi-class Stochastic Gradient Descent Dynamics of on-line gradient descent learning for multilayer neural networks

Reference 39

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Observation 6a9f716a-6a39-40f6-b100-620b1cecb8c5 · outbound

This paper cites Exact solution for on-line learning in multilayer neural networks.Physical Review Letters, 74(21):4337, 1995.

Exact Dynamics of Multi-class Stochastic Gradient Descent Exact solution for on-line learning in multilayer neural networks.Physical Review Letters, 74(21):4337, 1995

Reference 40

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Observation 1d808604-d48f-49d5-b3b7-56d3760c80d1 · outbound

This paper cites Random matrix theory proves that deep learning representations of gan-data behave as gaussian mixtures.

Exact Dynamics of Multi-class Stochastic Gradient Descent Random matrix theory proves that deep learning representations of gan-data behave as gaussian mixtures

Reference 41

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no resolver link, observed 2026-08-04T09:43:13.927373Z

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source=pdf_text observed=2026-08-04T09:43:13.927373Z digest=sha256:4bbe2d29d06456b7bb3f8d3f3528fa8b0158b6eb94a83a1e044bab3e30d70e1f

Observation c2c46541-cdc7-4cbd-b22a-4d2ac218a089 · outbound

This paper cites The implicit bias of gradient descent on separable data.Journal of Machine Learning Research, 19(70):1–57, 2018.

Exact Dynamics of Multi-class Stochastic Gradient Descent The implicit bias of gradient descent on separable data.Journal of Machine Learning Research, 19(70):1–57, 2018

Reference 42

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source=pdf_text observed=2026-08-04T09:43:13.996903Z digest=sha256:ad51f1dc772708594b6732974af5ef077b3dead6f8c50ab7fb6f713f69aad5bb

Observation 2bfc8f03-f74c-426f-b1dd-38207e5af8e4 · outbound

This paper cites Theoretical insights into multiclass classification: A high-dimensional asymptotic view.Advancesin Neural Information Processing Systems, 33:8907–8920, 2020.

Exact Dynamics of Multi-class Stochastic Gradient Descent Theoretical insights into multiclass classification: A high-dimensional asymptotic view.Advancesin Neural Information Processing Systems, 33:8907–8920, 2020

Reference 43

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Observation 308282da-29b1-498b-9f11-00bc0abb34d7 · outbound

This paper cites High-dimensional probability, volume 47 of Cambridge Series in Statistical and Probabilistic Mathematics.

Exact Dynamics of Multi-class Stochastic Gradient Descent High-dimensional probability, volume 47 of Cambridge Series in Statistical and Probabilistic Mathematics

Reference 44

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Observation beecbc06-f7e0-41f5-8703-868135500140 · outbound

This paper cites A solvable high-dimensional model of GAN.Advances in Neural Information Processing Systems, 32, 2019.

Exact Dynamics of Multi-class Stochastic Gradient Descent A solvable high-dimensional model of GAN.Advances in Neural Information Processing Systems, 32, 2019

Reference 45

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Observation 3f4f2410-360e-4d5b-83c9-ba29d89f0567 · outbound

This paper cites Data-dependence of plateau phenomenon in learning with neural network—statistical mechanical analysis.Advancesin Neural Information Processing Systems, 32, 2019.

Exact Dynamics of Multi-class Stochastic Gradient Descent Data-dependence of plateau phenomenon in learning with neural network—statistical mechanical analysis.Advancesin Neural Information Processing Systems, 32, 2019

Reference 46

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Pith citing papers

Observation 21607f55-cac6-4e44-b83d-2fe74efa4909 · inbound

Homogenization of $\ell_2$-Adversarial Training in High-Dimensions: Exact Dynamics under Stochastic Gradient Descent cites this paper.

Homogenization of $\ell_2$-Adversarial Training in High-Dimensions: Exact Dynamics under Stochastic Gradient Descent Exact Dynamics of Multi-class Stochastic Gradient Descent

Reference 18

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arxiv_id, observed 2026-07-14T03:22:45.605646Z

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