Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum | Paper Citation Record

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,

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:16:05.445325Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:16:05.422343Z

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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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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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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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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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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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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:16:05.346931Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:16:05.328386Z

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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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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

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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

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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

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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

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