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Paper Citation Record · LEDGER

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum

As of 20 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2505.10889.

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

pith.paper-citation-record.v1
2505.10889 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:16:05.092207Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1b5f86fc-1484-450f-b64d-78e01a15e9ff · outbound

This paper cites A stochastic approximation method,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum A stochastic approximation method,

Reference 1

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no resolver link, observed 2026-08-15T21:16:04.870210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7585abd5-81d3-41f0-8ca3-fcf92f29d43f · outbound

This paper cites Some methods of speeding up the convergence of iteration methods,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Some methods of speeding up the convergence of iteration methods,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation b807a7cb-5537-4f4d-a557-2a99fad654c7 · outbound

This paper cites Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis,

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 9e5d4f43-4c8a-4c92-a6fc-8b05117e671b · outbound

This paper cites Combining ordered subsets and momentum for accelerated X-ray CT image reconstruction,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Combining ordered subsets and momentum for accelerated X-ray CT image reconstruction,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation d25caf16-974f-4e6e-9b89-42683cde325b · outbound

This paper cites Speech recognition with deep recurrent neural networks,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Speech recognition with deep recurrent neural networks,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 2311f3d8-5143-4f18-ac09-c0031f7e3d24 · outbound

This paper cites SGD and Hogwild! convergence without the bounded gradients assumption,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum SGD and Hogwild! convergence without the bounded gradients assumption,

Reference 6

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raw_fallback, observed 2026-08-15T21:16:05.745177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 61dccd6c-5dc3-4985-a3e2-482351608cf1 · outbound

This paper cites Reducing the dimensionality of data with neural networks,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Reducing the dimensionality of data with neural networks,

Reference 7

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no resolver link, observed 2026-08-15T21:16:04.917004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:16:04.917004Z digest=sha256:3287c96529ea7c872c4d077e643a0b6949d9edc591d8b1c4df002fac1655b077

Observation 28293377-7842-44e8-bbee-1a415bf18e64 · outbound

This paper cites Distributed training strategies for the structured perceptron,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Distributed training strategies for the structured perceptron,

Reference 8

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raw_fallback, observed 2026-08-15T21:16:05.712772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 09b2f917-3779-4625-9eea-956a35f780f4 · outbound

This paper cites Deep learning with elastic averaging sgd,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Deep learning with elastic averaging sgd,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:04.928793Z digest=sha256:4c2ce9f34c3822835b612c33c1a531e03a749ebd17f790e7188f94c2621c5646

Observation be118f36-a1fd-4cd2-adfc-7a1d16366ecf · outbound

This paper cites Network topology and communication-computation tradeoffs in decentralized optimization,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Network topology and communication-computation tradeoffs in decentralized optimization,

Reference 10

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raw_fallback, observed 2026-08-15T21:16:05.677857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 8ba79a71-f9ed-4ec2-b28a-5e96becd7255 · outbound

This paper cites Local SGD Converges Fast and Communicates Little.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Local SGD Converges Fast and Communicates Little

Reference 11

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no resolver link, observed 2026-08-15T21:16:04.939109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:16:04.939109Z digest=sha256:d37a35f09770eb95292617f3f21b6a7aeb834ac062034773fef29a4bdfdfdbea

Observation 3c49b6d1-4c9e-4dce-994f-4bbc64568414 · outbound

This paper cites Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning,

Reference 12

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raw_fallback, observed 2026-08-15T21:16:05.658384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation e04901f1-2fae-44e9-86a5-c19c7cef8610 · outbound

This paper cites A linear speedup analysis of distributed deep learning with sparse and quantized communication,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum A linear speedup analysis of distributed deep learning with sparse and quantized communication,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation d7cc35b9-7ac1-4333-b872-59d8a830cf17 · outbound

This paper cites Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent,

Reference 14

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no resolver link, observed 2026-08-15T21:16:04.959122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:16:04.959122Z digest=sha256:b3beede8eeaa3e694ecc614338c3c34bd2be2e6b6893dab776895186f217a910

Observation bd5fc640-7e5a-4828-8cea-377fc323f43a · outbound

This paper cites Collaborative deep learning in fixed topology networks,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Collaborative deep learning in fixed topology networks,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 54d6075a-44b8-4aa7-89e9-dae226ecf6aa · outbound

This paper cites On nonconvex decentralized gradient descent,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum On nonconvex decentralized gradient descent,

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:16:04.970086Z digest=sha256:4537a937029d2c737b8924478db30e52fdc94d20de8bd00081e74bbc04fba06b

Observation 21478628-8e38-4c4f-bc5a-787d95886490 · outbound

This paper cites Cooperative sgd: A unified framework for the design and analysis of local-update sgd algorithms,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Cooperative sgd: A unified framework for the design and analysis of local-update sgd algorithms,

Reference 17

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 5968c1dd-000c-4260-85ef-567626f646cd · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Imagenet classification with deep convolutional neural networks,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 063a5a98-a5ae-47e7-bb83-de7c48a3e4b8 · outbound

This paper cites A Unified Analysis of Stochastic Momentum Methods for Deep Learning.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum A Unified Analysis of Stochastic Momentum Methods for Deep Learning

Reference 19

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no resolver link, observed 2026-08-15T21:16:04.987777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:16:04.987777Z digest=sha256:1c05e80a03b87191130d6eeb8fb6c174beac2631b3a49552767f80be7606754a

Observation 22d8a116-b5a8-472d-827d-3cd5064a03e6 · outbound

This paper cites On the importance of initialization and momentum in deep learning,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum On the importance of initialization and momentum in deep learning,

Reference 20

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raw_fallback, observed 2026-08-15T21:16:05.542612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation d3c4ab34-6398-4381-a1d0-c753a7476d50 · outbound

This paper cites On the linear speedup analysis of communication efficient momentum sgd for distributed non- convex optimization,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum On the linear speedup analysis of communication efficient momentum sgd for distributed non- convex optimization,

Reference 21

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raw_fallback, observed 2026-08-15T21:16:05.524880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.001300Z digest=sha256:e29f2ce9564766cb8d0bb76faf749e0b4c4f0419fb65c55898f6274293e1fd1a

Observation 615cb8d7-1509-4876-838e-b19ce8728ba0 · outbound

This paper cites On consensus- optimality trade-offs in collaborative deep learning,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum On consensus- optimality trade-offs in collaborative deep learning,

Reference 22

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raw_fallback, observed 2026-08-15T21:16:05.507346Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 3be9fce3-33cf-4350-af62-f99cc5b9cbdc · outbound

This paper cites Deep gradient compression: Reducing the communication bandwidth for distributed training,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Deep gradient compression: Reducing the communication bandwidth for distributed training,

Reference 23

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raw_fallback, observed 2026-08-15T21:16:05.484749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 351bc966-cd6c-4988-b96b-29baf7d61ca0 · outbound

This paper cites Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:16:05.463015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.016562Z digest=sha256:ca82bc524035f523e0a904fa4b73ca4ffb5800f1059d2bf8357a910804787aee

Observation 2af36f5a-24eb-46ea-b0fa-ca8a190c034b · outbound

This paper cites Decentlam: Decentralized momentum sgd for large-batch deep training,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Decentlam: Decentralized momentum sgd for large-batch deep training,

Reference 25

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raw_fallback, observed 2026-08-15T21:16:05.445325Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 800f32fa-3c7f-41c4-a694-f859fd295700 · outbound

This paper cites 2020SQuARM: Communication-efficient momentum SGD for decentralized optimization,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum 2020SQuARM: Communication-efficient momentum SGD for decentralized optimization,

Reference 26

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raw_fallback, observed 2026-08-15T21:16:05.422343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.026972Z digest=sha256:187fef122c9f5ed9ffd5cb2d985f9a6dbde1e6235b453720b5ca42626cb0b4e5

Observation 03643d56-567b-42d2-bde3-7f2e2674825f · outbound

This paper cites Periodic Stochastic Gradient Descent with Momentum for Decentralized Training.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Periodic Stochastic Gradient Descent with Momentum for Decentralized Training

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:16:05.031704Z digest=sha256:4a457724b5e93c33b2348b5e2a54c01a5425375578c220d7f3065722bd577c89

Observation e6b9600d-3953-418e-a5ae-5b625c772bdc · outbound

This paper cites Decentralized deep learning using momentum-accelerated consensus,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Decentralized deep learning using momentum-accelerated consensus,

Reference 28

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raw_fallback, observed 2026-08-15T21:16:05.403893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.037280Z digest=sha256:a90c6beb3aac5ddf1f5b09e851af89c089029c72afca386d07f0a31e51a3333c

Observation 2039e531-6c49-4595-b67b-0fedeebd52be · outbound

This paper cites Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning,

Reference 29

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raw_fallback, observed 2026-08-15T21:16:05.384740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 58a2e080-9d1c-455e-84a1-8ad434aad158 · outbound

This paper cites On the convergence of mSGD and AdaGrad for stochastic optimization,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum On the convergence of mSGD and AdaGrad for stochastic optimization,

Reference 30

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raw_fallback, observed 2026-08-15T21:16:05.365253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.047649Z digest=sha256:7792c861316079663b72da5ab9dc0183bb229a45986aba6926cc8568bddf83c8

Observation 4cbe6bf7-67b3-45b1-bdbf-c4747bbdeefc · outbound

This paper cites Don't Decay the Learning Rate, Increase the Batch Size.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Don't Decay the Learning Rate, Increase the Batch Size

Reference 31

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no resolver link, observed 2026-08-15T21:16:05.053234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:16:05.053234Z digest=sha256:7f46ff827cc2e9ec6af34a82ee3f2dd246f344c922b8512672371c497f03cf1e

Observation 696e5d03-2482-4598-9f3f-27ea1dba391d · outbound

This paper cites Bayesian learning via stochastic gradient langevin dynamics,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Bayesian learning via stochastic gradient langevin dynamics,

Reference 32

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raw_fallback, observed 2026-08-15T21:16:05.346931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.059295Z digest=sha256:d84473b8b778e4ef49a6e79d20f14aec0dc50db4b208db383f45f2f339a2ffeb

Observation cfc784d5-d69d-4e7c-9cc0-f008fbdc5273 · outbound

This paper cites Convergence of proximal-gradient stochastic variational inference under non-decreasing step-size sequence,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Convergence of proximal-gradient stochastic variational inference under non-decreasing step-size sequence,

Reference 33

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raw_fallback, observed 2026-08-15T21:16:05.328386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.065059Z digest=sha256:a08718da045260354e4b13222d4d2206004ca332cef65d0aee5ddf187ce508e7

Observation d656d89c-cd16-4138-b152-92cb33613746 · outbound

This paper cites Understanding the role of momentum in stochastic gradient methods,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Understanding the role of momentum in stochastic gradient methods,

Reference 34

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raw_fallback, observed 2026-08-15T21:16:05.308628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.070661Z digest=sha256:73d14f62553f2f630efd79946d1c693e8acae058a945b62e185d61e1e734faea

Observation a5415d8d-8810-476f-b99f-1868005ba493 · outbound

This paper cites Deep residual learning for image recognition,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Deep residual learning for image recognition,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:16:05.288603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.075459Z digest=sha256:5393dd6c35883769a3d9dae9e65d444dd7d303fb853b42d699b6d02be776f31f

Observation 0214823a-791f-4ca3-93aa-1578f2ad0091 · outbound

This paper cites Nesterov,Introductory Lectures on Convex Optimization: A Basic Course.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Nesterov,Introductory Lectures on Convex Optimization: A Basic Course

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:16:05.266884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.081368Z digest=sha256:bab775ed5ae90e4c9ee2b046aca2e258eb79aff917e04ba2869794bfc98b0b64

Observation e8d7a3ba-bd4b-439c-8769-b5e6561f3fe8 · outbound

This paper cites Revisit last- iterate convergence of msgd under milder requirement on step size.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Revisit last- iterate convergence of msgd under milder requirement on step size

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:16:05.245733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.086621Z digest=sha256:3555ce178d08bbaac88dab072fb13335dc2adb67dc803e5da0f783c723e239de

Observation 4d126e17-d5ef-42e8-9b5e-e2e73f0ad451 · outbound

This paper cites On almost sure convergence for sums of stochastic sequence,.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum On almost sure convergence for sums of stochastic sequence,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:16:05.225176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:16:05.092207Z digest=sha256:538febdcf4cc3ee389d1832b26201e284fa7943ed682d0014b05f230a621eeaa

Pith citing papers

No inbound Pith citation observations are available.