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Paper Citation Record · LEDGER

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures

As of 7 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 2 inbound Pith citation observations for arXiv:2506.06584.

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

pith.paper-citation-record.v1
2506.06584 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:04:07.556292Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T23:40:46.820775Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:29:30.804420Z

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 824ae9c9-2fc9-4dea-84c4-18802d551d3d · outbound

This paper cites an unresolved cited work.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Unresolved cited work

Reference 1

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Observation a086df68-1b8d-449c-bed4-1473a109c427 · outbound

This paper cites Kakade, and Matus Telgarsky.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Kakade, and Matus Telgarsky

Reference 2

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Observation b5544151-21dd-4a03-b5ca-756c05eff304 · outbound

This paper cites Learning time-scales in two-layers neural networks.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Learning time-scales in two-layers neural networks

Reference 3

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Observation a4998754-753e-4d43-a920-baff9a346197 · outbound

This paper cites Stochastic approximation: a dynamical systems viewpoint , volume 9.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Stochastic approximation: a dynamical systems viewpoint , volume 9

Reference 4

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Observation 2c071eb5-c3ce-4474-adad-e0b67ce530f9 · outbound

This paper cites Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime

Reference 5

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T06:04:02.505287Z digest=sha256:7eabcfa3f4429bddf69363b121ef2cd577d8dccd30b47f739ab63ad59b30e1b1

Observation bf441ac8-d070-4847-bf09-8d23ce55d731 · outbound

This paper cites Mirror descent and nonlinear projected subgradient methods for convex optimization.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Mirror descent and nonlinear projected subgradient methods for convex optimization

Reference 6

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Observation 59554b2e-f724-4cdc-a275-f905f4a4aff0 · outbound

This paper cites Wainwright, and Bin Yu.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Wainwright, and Bin Yu

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:02.783778Z digest=sha256:420e1e809d72a59d6c2565fc02e452ae76493eea5ef38615803b87db673a0a43

Observation 6005686a-acd7-49d8-9283-21bfc6628219 · outbound

This paper cites Extreme ratio between spectral and frobenius norms of nonnegative tensors.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Extreme ratio between spectral and frobenius norms of nonnegative tensors

Reference 8

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Source-reported events for the cited work

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Observation a4b87d9f-ef0b-4dea-bd31-a094a4f1dbb2 · outbound

This paper cites Local minima structures in gaussian mixture models.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Local minima structures in gaussian mixture models

Reference 9

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Observation 7f9f9602-da01-43b8-95f4-f5a97bcb46e5 · outbound

This paper cites Learning mixtures of gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Learning mixtures of gaussians

Reference 10

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Source-reported events for the cited work

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Observation 58cc5545-eb44-4cf3-8bf9-405ce5e85cbc · outbound

This paper cites Singularity, misspecification and the convergence rate of EM.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Singularity, misspecification and the convergence rate of EM

Reference 11

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Observation 1b953d37-589f-44aa-9af9-3ab1161b59a6 · outbound

This paper cites Wainwright, and Michael I.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Wainwright, and Michael I

Reference 12

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Source-reported events for the cited work

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Observation c1d5f879-5eff-40e2-a72f-f263363d4de5 · outbound

This paper cites Wainwright, Michael I.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Wainwright, Michael I

Reference 13

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Observation 6091336c-0796-40a1-87f9-4e67f084f6fb · outbound

This paper cites Maximum likelihood from incomplete data via the em algorithm.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Maximum likelihood from incomplete data via the em algorithm

Reference 14

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Observation a384c81f-1ff1-43e9-a43f-227828925dc8 · outbound

This paper cites Schulman.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Schulman

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 97742c8e-64f2-43a6-b6e8-0e4c88a3b424 · outbound

This paper cites Ten steps of EM suffice for mixtures of two gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Ten steps of EM suffice for mixtures of two gaussians

Reference 16

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Source-reported events for the cited work

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Observation bcbac740-ab5f-48fb-9efd-c7bb26c7d5d0 · outbound

This paper cites A kernel two-sample test.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures A kernel two-sample test

Reference 17

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Observation b1e6a91a-7377-4248-8564-3c2c68a5640e · outbound

This paper cites Learning mixtures of gaussians in high dimensions.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Learning mixtures of gaussians in high dimensions

Reference 18

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Observation 4b7dcbf0-bd09-43c1-98d9-f760d8868f93 · outbound

This paper cites Learning mixtures of spherical gaussians: moment methods and spectral decompositions.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Learning mixtures of spherical gaussians: moment methods and spectral decompositions

Reference 19

Resolution
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Source-reported events for the cited work

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Observation 295140d0-7d05-4711-aa0b-4aab946bd768 · outbound

This paper cites Mixture models, robustness, and sum of squares proofs.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Mixture models, robustness, and sum of squares proofs

Reference 20

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T06:04:04.397134Z digest=sha256:97f56630cdc2a8c213673d26e055886596fed7067868a8cb73d548f5956f9955

Observation 641cdf8f-8600-419e-8be9-0c40beb9f40d · outbound

This paper cites Revisiting frank-wolfe: Projection-free sparse convex optimization.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Revisiting frank-wolfe: Projection-free sparse convex optimization

Reference 21

Resolution
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Observation 4a03c832-88a3-4837-a40d-f3baad60343e · outbound

This paper cites Local maxima in the likelihood of gaussian mixture models: Structural results and algorithmic consequences.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Local maxima in the likelihood of gaussian mixture models: Structural results and algorithmic consequences

Reference 22

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Source-reported events for the cited work

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Observation 476a9491-c384-498b-95eb-f0d2d6129dfc · outbound

This paper cites Robust learning of mixtures of gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Robust learning of mixtures of gaussians

Reference 23

Resolution
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Source-reported events for the cited work

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Observation 71692856-5012-4a81-bbf2-121238f8fc78 · outbound

This paper cites The EM algorithm gives sample-optimality for learning mixtures of well-separated gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures The EM algorithm gives sample-optimality for learning mixtures of well-separated gaussians

Reference 24

Resolution
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Observation 92d3baa4-9f38-4c98-920a-a5543db1cb49 · outbound

This paper cites Efficiently learning mixtures of two gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Efficiently learning mixtures of two gaussians

Reference 25

Resolution
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Source-reported events for the cited work

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Observation e1b90d5d-c4b8-48d8-be59-610e81538c45 · outbound

This paper cites Robust moment estimation and improved clustering via sum of squares.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Robust moment estimation and improved clustering via sum of squares

Reference 26

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3a33be04-7278-4ff3-aced-1874d935f8be · outbound

This paper cites Homotopy analysis method in nonlinear differential equations , volume 153.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Homotopy analysis method in nonlinear differential equations , volume 153

Reference 27

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation a9adb427-a478-449f-aa84-56475759ae90 · outbound

This paper cites Clustering mixtures with almost optimal separation in polynomial time.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Clustering mixtures with almost optimal separation in polynomial time

Reference 28

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d908b2f1-40c0-4aa1-9e93-004560b3b991 · outbound

This paper cites Robustly learning general mixtures of gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Robustly learning general mixtures of gaussians

Reference 29

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation f0888ada-818d-4f23-b22d-9c47e7c71cf7 · outbound

This paper cites On orthogonal tensors and best rank-one approximation ratio.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures On orthogonal tensors and best rank-one approximation ratio

Reference 30

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 968b8d70-d496-4790-bc50-c8db3cf80089 · outbound

This paper cites Leveraging the two-timescale regime to demonstrate convergence of neural networks.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Leveraging the two-timescale regime to demonstrate convergence of neural networks

Reference 31

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c9b9b18a-5745-43d7-bcf8-d3a1e2bde9fe · outbound

This paper cites On relations between the relative entropy and 2-divergence, generalizations and applications.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures On relations between the relative entropy and 2-divergence, generalizations and applications

Reference 32

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:05.677202Z digest=sha256:b17ebba582ce46cda129c821130fa90238327e010af7868bbf9900dce5939469

Observation 9b31119f-31af-4fc0-a7a2-18591464cbb8 · outbound

This paper cites Primer on monotone operator methods.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Primer on monotone operator methods

Reference 33

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:05.795518Z digest=sha256:47bffc2c1aab925d8987c73034c3018a59486b640c76e4599eedb346b672931c

Observation 7bc2f3a0-a86b-45f5-a8a5-16b4f379dc71 · outbound

This paper cites On learning mixtures of well-separated gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures On learning mixtures of well-separated gaussians

Reference 34

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:05.904203Z digest=sha256:3cb7854427228f613ba6d1d586598da3c267f6ff1a539489b117b1fc90a448c4

Observation 97313328-7255-43a7-8da3-d178a5149b35 · outbound

This paper cites Improved convergence guarantees for learning gaussian mixture models by em and gradient em.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Improved convergence guarantees for learning gaussian mixture models by em and gradient em

Reference 35

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raw_fallback, observed 2026-08-07T06:04:11.464093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:06.037835Z digest=sha256:86d06fe1ee336bf2d094f5172cabe2ac877f2e5f7a0b96458264c11cbc6004da

Observation 06b61067-21f9-44b6-aecd-856c68fb917d · outbound

This paper cites Estimation of the mean of a multivariate normal distribution.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Estimation of the mean of a multivariate normal distribution

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:11.199385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:06.149417Z digest=sha256:b6d162a88b7de8c2f30c15d50b612e3c2045f95ba457b6535052f4beb845df3e

Observation ab3c40d5-6c23-4b72-a861-50b7dc97cd8b · outbound

This paper cites Mean-field analysis on two-layer neural networks from a kernel perspective.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Mean-field analysis on two-layer neural networks from a kernel perspective

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:10.878067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:06.248439Z digest=sha256:22045915349bc9061c6a708eeb6bed2b1c3d203f2a525db50c36cd59d8671f2f

Observation e0ad55d3-9617-428c-91c4-7fc697b0d19f · outbound

This paper cites The EM algorithm is adaptively-optimal for unbalanced symmetric gaussian mixtures.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures The EM algorithm is adaptively-optimal for unbalanced symmetric gaussian mixtures

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:10.636896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:06.395920Z digest=sha256:835ca959e95ca4ab3fec0af3c9d615dab2ac34e3d578a7a4042faa0a422727a6

Observation b8dafbd1-4c62-49bd-ac0a-c533dd0777de · outbound

This paper cites On the convergence properties of the em algorithm.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures On the convergence properties of the em algorithm

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:10.350087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:06.500410Z digest=sha256:36378cfe897c4f49b311b121413dc5f485c4807257e27029102970ff466bec13

Observation 7f0895c1-8de6-45f3-8710-d7d312b89a83 · outbound

This paper cites Randomly initialized EM algorithm for two-component gaussian mixture achieves near optimality in o( n ) iterations.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Randomly initialized EM algorithm for two-component gaussian mixture achieves near optimality in o( n ) iterations

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:10.064279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:06.621851Z digest=sha256:6ce04a52245b79858ee6083d83b3812db9e20449f75c63df186f18fb1615d209

Observation 8da91c6c-1c49-4746-9745-9a50d90272b4 · outbound

This paper cites Over-parameterization exponentially slows down gradient descent for learning a single neuron.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Over-parameterization exponentially slows down gradient descent for learning a single neuron

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:09.782385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:06.746672Z digest=sha256:e2075543b672b27811c51b219fc4bcf75fac70681cc4cdf4b0a71f9345bbef2b

Observation 81d64e69-5931-4bf4-a476-e76b7a2cc81b · outbound

This paper cites Toward global convergence of gradient EM for over-paramterized gaussian mixture models.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Toward global convergence of gradient EM for over-paramterized gaussian mixture models

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:09.492336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:06.843898Z digest=sha256:ce4d83454f3f4462434d9657eea39a532bce6d5197e212904593acf79d8cbd62

Observation cc660863-0db0-410d-8cb9-ee96c2f9af71 · outbound

This paper cites Hsu, and Arian Maleki.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Hsu, and Arian Maleki

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:09.218392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:06.997705Z digest=sha256:99d908b06e1ea53e827c3e6961beeae055c936d4b51719ace3f943b405fa1ea3

Observation 77da29f8-8043-491c-897a-d93d7cb615ab · outbound

This paper cites On convergence properties of the EM algorithm for gaussian mixtures.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures On convergence properties of the EM algorithm for gaussian mixtures

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:08.973383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:07.071917Z digest=sha256:5fae8328beb0ec3a8fbc037593351f4e7f44bd648e728d36e765797e738d5bbf

Observation 61a888bc-77a4-496c-98a6-3d73ab22dff2 · outbound

This paper cites Convergence of gradient em on multi-component mixture of gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Convergence of gradient em on multi-component mixture of gaussians

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:08.706725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:07.196679Z digest=sha256:023342eabb467aed2b1ed7f16db2ae1a66da034b6c1f00288323c4b19c0f3398

Observation debc93fa-0b87-4722-9441-590d76613bfe · outbound

This paper cites How does gradient descent learn features --- a local analysis for regularized two-layer neural networks.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures How does gradient descent learn features --- a local analysis for regularized two-layer neural networks

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:08.406989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:07.291530Z digest=sha256:4d0e35ce666125ab85c2df1f78602494751689353676af552d73d9e0036c0180

Observation 8135a29b-2a7f-4451-a803-a0afd5d30dd5 · outbound

This paper cites A local convergence theory for mildly over-parameterized two-layer neural network.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures A local convergence theory for mildly over-parameterized two-layer neural network

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:08.089498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:07.399002Z digest=sha256:103d4d8cdeffbf4dbd8e4be3385d54ce8997d06480bc91a28f418c829792d967

Observation ae1f0ac5-35c5-4a5f-91c1-fb2d34b670c2 · outbound

This paper cites Statistical convergence of the em algorithm on gaussian mixture models.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Statistical convergence of the em algorithm on gaussian mixture models

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T06:04:07.845037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T06:04:07.556292Z digest=sha256:0f66585d398822778629969d493e15efa00d33b499baa46f1e5e97ee41b6efce

Pith citing papers

Observation ee04510f-e9f9-4d94-a4da-d433b2afe4aa · inbound

Local linear convergence of gradient methods for overparameterized Gaussian mixtures cites this paper.

Local linear convergence of gradient methods for overparameterized Gaussian mixtures Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-06-28T23:42:49.405457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-28T23:40:46.820775Z digest=sha256:638d2066dae7f86121230f21e8d76d7f3926ae012977721047c98db43b5beb1a

Observation fc09f6a2-dbb4-4a6b-ae9e-176cf59fcc0d · inbound

Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence cites this paper.

Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-07-04T03:29:30.806812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T17:59:23.282231Z digest=sha256:574828d0cd650fa66901b0aa6bc43c8f6fb0bedb09b5f9e78fe580463524e378